Modelling transmission of Middle East respiratory syndrome coronavirus in camel populations and the potential impact of animal vaccination

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With two vaccine candidates shown to reduce infectiousness, there is a need to better understand transmission of MERS-CoV in camels and assess the potential impact of vaccination. To help address this, we used age-stratified seroprevalence data and a combination of modelling methodologies to estimate key epidemiological quantities including MERS-CoV transmissibility in camels and to estimate vaccine impact on infection incidence. Transmissibility was higher in the Middle East ( R 0 range 3–34) compared to Africa (2–15) and South Asia (2–4), highlighting the need for setting-specific vaccination strategies. Modelling suggested that even if the vaccine only reduced infectiousness rather than susceptibility to infection, vaccinating calves could achieve large reductions in incidence in moderate and high transmission settings, and interrupt transmission in low transmission settings, provided coverage was high (70–90%). Health sciences/Diseases/Infectious diseases/Viral infection Health sciences/Diseases/Respiratory tract diseases MERS-CoV camels vaccination mathematical modelling zoonotic disease prevention Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Middle East respiratory syndrome coronavirus (MERS-CoV) causes severe acute respiratory disease in humans, with an estimated infection fatality ratio of approximately 22% 1 . Although capable of spreading rapidly in hospital settings 2 , 3 , MERS-CoV transmission is inefficient in the general community and recurrent outbreaks in humans are driven by repeated zoonotic spillover from dromedary camels ( Camelus dromedarius ) 4 from here on referred to as “camels”. The role of camels in ongoing transmission has led to demand for camel vaccination as part of a set of interventions to avert human cases 5 . With two vaccine candidates shown to reduce viral shedding 6 , 7 , there is a need to assess the potential impact of camel vaccination on transmission. This is hindered, however, by poor understanding of the epidemiology of MERS-CoV amongst camels and a lack of mathematical models of transmission dynamics in the animals. Most camels show no outward signs of MERS-CoV infection, making assessing the epidemiology of the virus in the zoonotic reservoir challenging. Cross-sectional surveys testing for antibodies against MERS-CoV or viral RNA have demonstrated that as well as being endemic in camel populations in the Middle East where autochthonous human MERS cases are reported, the virus is present in camels in parts of South Asia and circulates widely across Africa where the majority of the world’s camels reside 8 , 9 . Little is known about transmission intensity and how this varies within the global camel population which is highly heterogeneous in terms of structure and husbandry practices. Only one study has estimated the annual force of infection, estimating it to be 0.4 in ranch populations and 0.1-1.0 in pastoral populations in Kenya 10 . Knowledge of immunity is also limited, but longitudinal studies have provided two important insights. Firstly, studies in a small number of mother-calf pairs observed a wave of infection sweeping through calf populations after maternally-acquired antibodies (mAbs) waned over the first few months of life, suggesting protective mAbs may play a role in infection dynamics 11 , 12 . Secondly, studies have demonstrated reinfection of previously seropositive animals, and rapid reinfection of infected animals in high density settings such as markets and holding pens 6 , 13 , 14 . Unfortunately, longitudinal studies have been too short or small to reliably measure how immunity and calving might lead to seasonal variation in infection. Evidence from phylogenetic analysis suggests the risk of spillover is highest between April and July 4 , but this is not reflected by the incidence of primary cases 15 . These knowledge gaps around transmission intensity and immunity, taken together with the lack of mathematical models of transmission in camels, impede the design of informed animal vaccination strategies, and the evaluation of their potential impact. Here, we use published age-stratified seroprevalence data from camel populations across Africa, the Middle East, and South Asia to fit catalytic models of seroconversion and produce population specific estimates of MERS-CoV transmissibility in camels. We then introduce a stochastic, age-structured, dynamic transmission model of MERS-CoV in camels, which we use to estimate key, epidemiological quantities, including R 0 , the Critical Community Size (CCS) and periodicity of infections, thereby providing insights into how controllable MERS-CoV may be in different camel populations. Finally, we use our model to simulate vaccination assuming different efficacy scenarios. We evaluate the potential impact of vaccination on transmission in camels, as well as the optimal age for vaccination. Alongside empirical studies, insights from dynamic models such as those developed here could contribute to informing an effective response to the zoonotic transmission of MERS-CoV. Results Transmissibility of MERS-CoV in camel populations Force of infection (FoI) To estimate the FoI, we fitted four different models of seroconversion to age-stratified seroprevalence data extracted in our previous systematic review of MERS-CoV in camels 8 , assuming the seroprevalence data was beta-binomially distributed. We found that allowing a proportion of calves to be born with protective maternally acquired antibodies (mAbs) in model 3 improved model fit, as did the inclusion of seroreversion due to waning of antibodies acquired following infection in model 2, though the additional improvement in fit afforded by seroreversion on top of mAbs in the best fitting model (model 4) was fairly small ( Table S1 ). We assumed test sensitivity and specificity were high for both neutralisation and non-neutralisation-based tests. The ranking of model fit was robust to the use of alternative assumptions where sensitivity of neutralisation tests was modelled to be lower at 85% ( Table S2 ). Model selection was also generally robust to exclusion of each dataset from the analysis, with the model with mAbs alone or with mAbs and seroreversion outperforming the others (Figure S2). Parameters governing rates of antibody waning and data overdispersion were estimated as common to all studies, while the FoI was allowed to be study-specific. We estimated that mAbs waned rapidly in the first few months of life, lasting on average 2 months (95% credible interval (CrI): 1–4 months) in our best fitting model, but that antibodies wane slowly following infection, lasting approximately 17 years (95% CrI: 9–33 years) – approaching the lifespan of camels. However, it can be difficult to distinguish life-long antibodies from repeated boosting using catalytic models. Parameter estimates were largely robust to exclusion of each data set with the exception of the data collected in Egypt which, when excluded, increased the estimated duration of mAbs to 4.8 months, similar to the 4.2 months estimated in model 3 ( Figure S3 ). The overdispersion parameter, k , was estimated to be 2.5 (95% CrI: 2.0, 3.2), meaning the variance of the data was estimated to be around 3.5 times greater than what would be expected if the data were binomially distributed. When an uninformative prior for k was used, the model tended to maximise k meaning an extreme level of overdispersion could on its own account for the patterns observed in the data, irrespective of other epidemiological parameter values which were then unidentifiable. To circumvent this issue, a half normal prior with a standard deviation of 0.5 was used to constrain k to values we believe to be more plausible. The annual FoI of MERS-CoV in camels was generally higher in populations sampled in the Middle East, and lower in those sampled in South Asia and Africa (Fig. 1 , Table 1 ). This trend was consistent across all four models ( Table S3). The posterior mode for the FoI ranged from 0.1-3.0 across most study populations, except for in the population in UAE where seroprevalence was very high (> 85%) in both calves and adults, perhaps indicative of a recent outbreak, and the FoI was estimated to be 7.1/year. Such high FoI estimates are necessary to explain the high seroprevalence measured in younger animals when assuming endemic transmission, but it is important to note that there is a risk of over-estimation of FoI for populations with seroprevalence approaching 100% since all high FoI values fit the data equally well. This effect is likely reflected by the long tails of some of the posterior distributions presented in Fig. 1 A; we therefore chose to present the posterior mode as the central FoI estimates as we believe this to be a more representative than the mean. The best fitting model matched the data well, with model estimates overlapping the age-stratified seroprevalence data with only a few exceptions (Fig. 1 B). The very high levels of seroprevalence measured in young calves in Tunisia and Kingdom of Saudi Arabia (KSA) 16 were underestimated, and the model struggled to reproduce the data collected in one study in KSA where seroprevalence was very high in calves and dropped considerably in adults 17 , which was not seen in any other study. Table 1 Population specific estimates of the transmissibility of MERS-CoV in camels. Dataset Seroprevalence (%) Test* FoI, l Model 4: R 0 relative infectiousness of reinfections 1% 50% Africa Egypt 18 < 2yrs 37% (n = 595) ≥ 2yrs 82% (n = 1946) MN 0.5 (0.4, 0.7) 4.2 (3.7, 5.1) 1.9 (1.8, 2.1) Egypt 16 < 2yrs 16% (n = 447) ≥ 2yrs 84% (n = 1586) MN 0.5 (0.4, 0.6) 4.0 (3.5, 4.7) 1.9 (1.8, 2.0) Ethiopia 19 1- ≤2yrs 93% (n = 31) 2-13yrs 97% (n = 157) PM 2.7 (1.6, 9.7) 14.9 (9.6, 44.0) 3.0 (2.6, 4.9) Kenya 20 2yrs 61% (n = 194) PM 0.2 (0.2, 0.4) 2.6 (2.1, 3.5) 1.7 (1.4, 1.8) Kenya 21 1-4yrs 73% (n = 285) 4-6yrs 98% (n = 116) 6yrs 98% (n = 476) ELISA 1.0 (0.8, 2.3) 6.8 (5.5, 13.4) 2.3 (2.2, 2.9) Kenya 22 4 7yrs 82% (n = 760) ELISA 0.3 (0.2, 0.4) 2.9 (2.5, 3.7) 1.7 (1.6, 1.9) Senegal 16 < 2yrs 29% (n = 17) ≥ 2yrs 69% (n = 181) MN 0.3 (0.2, 0.6) 2.9 (2.3, 4.6) 1.7 (1.5, 2.0) Tunisia 16 < 2yrs 100% (n = 28) ≥ 2yrs 87% (n = 754) MN 0.8 (0.6, 2.2) 5.8 (4.6, 12.8) 2.2 (2.0, 2.8) Tunisia 19 < 2yrs 30% (n = 46) ≥ 2yrs 54% (n = 158) PM 0.2 (0.1, 0.3) 2.2 (1.8, 3.1) 1.5 (1.3, 1.7) Uganda 16 < 2yrs 52% (n = 150) ≥ 2yrs 66% (n = 350) MN 0.3 (0.2, 0.6) 3.2 (2.6, 4.5) 1.8 (1.7, 2.0) Middle East Iraq 16 < 2yrs 33% (n = 6) ≥ 2yrs 57% (n = 21) MN 0.2 (0.1, 7.5) 2.6 (1.7, 35.3) 1.7 (1.3, 4.3) Iraq 23 4yrs 86% (n = 78) ELISA 1.8 (0.9, 9.4) 10.6 (6.4, 43.0) 2.7 (2.3, 4.8) Jordan 16 2yrs 100% a (n = 14 a ) ELISA 2.6 (1.2, 9.5) 14.8 (7.8, 43.1) 3.0 (2.4, 4.8) KSA 25 1992–2010 ≤ 2yrs 55% (n = 104) > 2yrs 95% (n = 98) ELISA 1.8 (1.1, 7.8) 10.9 (7.2, 36.3) 2.7 (2.4, 4.4) KSA 25 2013 ≤ 2yrs 73% (n = 77) > 2yrs 93% (n = 187) ELISA 1.2 (0.7, 2.8) 7.5 (5.3, 15.7) 2.4 (2.1, 3.0) KSA 17 1-2yrs 93% (n = 71) 3-5yrs 78% (n = 100) ELISA 2.3 (1.2, 9.5) 13.2 (7.5, 43.1) 2.9 (2.4, 4.8) KSA 26 5yrs 92% (n = 63) ppNT 3.0 (1.7, 9.1) 16.6 (10.4, 41.7) 3.1 (2.6, 4.7) KSA 16 4yrs 96% (n = 310) ELISA 7.1 (3.5, 9.8) 33.7 (18.5, 44.5) 4.2 (3.2, 4.9) South Asia Bangladesh 27 10yrs 49% (n = 88) ELISA 0.1 (0.1, 0.2) 1.9 (1.7, 2.3) 1.4 (1.3, 1.5) Pakistan 29 ≤ 3yrs 58% (n = 177) 3.1-10yrs 79% (n = 712) > 10yrs 81% (n = 161) ELISA then MN 0.5 (0.4, 0.7) 3.9 (3.3, 4.9) 1.9 (1.8, 2.1) Global Rate of waning mAbs, w 4.9 (3.2, 9.6) Rate of waning Abs, s 0.06 (0.03, 0.11) Overdispersion, k 2.5 (2.0, 3.2) *MN = micro neutralisation test, ppNT = pseudo particle neutralisation test, PM = protein micro-array, ELISA = Enzyme linked immunosorbent Assay. Basic reproduction number Basic reproduction number (R 0 ) R 0 estimates can provide a more widely used intuitive measure of transmissibility which are more readily comparable with other diseases. We translated FoI into R 0 using a dynamic, age-stratified, stochastic model of MERS-CoV transmission (please see Methods for a detailed description of model assumptions). Estimates were sensitive to the assumed relative infectiousness of reinfected animals (Table 1 ). When reinfected animals were assumed to be 1% as infectious as animals experiencing a primary infection (based on viral shedding data from the control arm of the ChAdOx vaccine field study 6 ), central R 0 estimates ranged from 3 to 34 in the Middle East, compared with 2 to 15 in populations sampled in Africa and 2 to 4 in South Asia. As a sensitivity analysis, when infectiousness was assumed to be only halved in reinfections (based on assuming a logarithmic rather than linear relationship between the measured viral shedding and infectiousness), R 0 ranged from 2 to 4 in the Middle East, 1 to 3 in Africa, and 1 to 2 in South Asia. R 0 estimates for populations with very high FoI estimates were most sensitive to assumptions about immunity as, in these populations, a higher proportion of infections at endemic equilibrium are reinfections and are therefore affected by relative infectiousness parameters. Neither varying the duration of complete immunity following infection, nor the relative susceptibility of previously infected individuals had a considerable effect on R 0 estimates ( Table S4 ). When using estimates for the longer duration of mAbs (4.2 months) and for the FoI from the second-best fitting model without seroreversion, R 0 estimates were similar to those using best fitting model with seroreversion, albeit slightly lower ( Table S4 ). The critical community size (CCS) We used the transmission model to estimate the population size above which the extinction of MERS-CoV transmission by chance becomes unlikely - the critical community size (CCS) 30 . We considered three different transmissibility levels spanning our R 0 estimates across different settings: low (South Asia and parts of Africa), moderate (Kenya and parts of Middle East), and high (Ethiopia and parts of Middle East) ( Figure S4 ). The CCS varied between approximately 10,000–70,000 camels depending on the transmissibility, seasonality of calving and underlying herd structure assumed in the transmission model (Table 2 , Fig. 2 ). The CCS decreased as transmission intensity increased, except for when births were highly seasonal, and the population was modelled as homogeneous. In this case, high transmissibility resulted in a larger CCS than low or moderate transmissibility. This likely represents a complex interaction between seasonality, transmissibility, and accumulation of susceptible individuals. Transmission could be sustained in smaller populations when births were less seasonally forced, and when the population was assumed to be homogenous as opposed to being structured into weakly connected patches intended to represent large herds or communities. Under our alternative assumption that viral shedding is proportional to the log of infectiousness meaning past infection reduces infectiousness by only 50%, the CCS was smaller, with only 1,000–30,000 camels needed to sustain transmission across depending on the transmission setting (Table 2 , Fig. 2 ). Table 2. The estimated critical community size of MERS-CoV in camels under two alternative values for the relative infectiousness of reinfected animals (r inf ), in different transmission settings (R 0 ). Estimates are shown for models assuming a homogeneous population of perfectly mixed animals and a structured population in which animals have more contact with those in their “patch” and weaker contact with those in surrounding patches. Estimates are shown assuming births follow seasonal patterns as strong as those observed in KSA (d = 1) and alternatively with a weaker seasonality (d = 0.5). Periodicity of transmission When births are assumed to follow seasonal patterns representative of those observed in KSA (see Methods ), the number of infections over time has an annual periodicity in large populations, with peaks of a similar size occurring each year (Fig. 3 .A). In small populations, or when transmissibility is low, reflecting estimates for camel populations in South Asia and parts of Africa, biennial, triennial, and quadrennial periodicities - with patterns in the magnitude of annual peaks in infections repeating over 2-, 3- or 4-year cycles – are detected based on autocorrelation coefficients in a proportion of stochastic iterations (Fig. 3 .B). No seasonality in infections is observed when births are non-seasonal. The impact of vaccination Optimal target age We extended the transmission model to simulate the impact of age-targeted vaccination under multiple efficacy scenarios based on RNA shedding data from field studies and remaining uncertainties ( Methods ). The optimal target age for routine vaccination was assessed in a large population of camels so that overarching trends were not obscured by stochasticity. In our conservative scenario (scenario 1), in which vaccination reduces infectiousness of subsequent infections in all vaccinated animals but had no effect on susceptibility, vaccination led to the greatest reduction in infection incidence when calves were targeted in the first few months of life (Fig. 4 A). In the absence of vaccination, most animals were first infected when they were < 1-yr old across all modelled transmission settings. In targeting younger calves, vaccination precedes first infection in a greater number of individuals, reducing their subsequent infectiousness. The reduction in incidence afforded by targeting younger animals was larger in higher transmission settings where first infections occurred earlier, with reductions in incidence diminishing quicker as the target age class was increased compared to in lower transmission settings. When the duration of vaccine induced effects was assumed to be relatively short and transmission intensity was moderate or high, vaccinating very young calves shifted the average time to first infection into older age groups, leading to a small increase in the annual incidence in adult animals of up to 10 per 1000 animals under our core model assumptions ( Figure S6 ). Vaccinating at 6 months allowed large reductions in overall incidence of infection without seeing considerable shifting of first infections into adult animals. Note that adult incidence is considered here given it may be a proxy for zoonotic spillover risk to humans. Under our optimistic scenario (scenario 2) in which we assumed vaccination reduced both infectiousness and susceptibility in all vaccinated animals, we saw the same pattern as in scenario 1, with greatest impact achieved by vaccinating in the first few months of life, and no notable increase in adult incidence when targeting 6-month-olds. Vaccinating older age groups after most first infections had occurred had almost no effect on incidence in scenario 1, whereas a slight reduction was still achieved by reducing the susceptibility of the older animals to reinfection in scenario 2 ( Figure S7 ). In our third scenario, in which vaccination was only effective as a booster for previously infected animals with no impact in naïve animals, the optimal age for vaccination was early adulthood but even then, the reduction in incidence was minimal at < 8% across all transmission intensities ( Figure S7). The optimal target age for vaccination was robust to our different assumptions about the relationship between viral load and infectiousness. The impact of vaccination on transmission To explore the characteristics of the MERS-CoV vaccine and the vaccine coverage that would be necessary to achieve considerable reductions in infection incidence among camels, we simulated the impact of vaccination of 6-month-old calves in two modelled populations: one of 2 million camels comparable in size to that of KSA; and one of 75,000 camels comparable to that of a small camel-keeping Kenyan county. In a population of 2 million camels divided into large homogenous patches, assuming the vaccine reduces infectiousness but not susceptibility, the vaccination coverage required to half the total incidence over the 10 years following introduction was between 50–90% in 6-month-olds, depending on the duration of vaccine induced effects and the transmission intensity (Fig. 4 B). When vaccine induced effects were long lasting, 50% coverage was required to half incidence in low transmission intensity settings, rising to approximately 80% coverage needed in high transmission intensity settings (Fig. 4 B). When effects lasted 3 years, coverage of 70% was needed in low transmission settings rising to 90% when transmission intensity was high, and when vaccine induced effects only lasted one year, incidence could not be halved under any modelled setting. Alternatively, in a population of 75,000 camels, stochastic effects amplified the impact of vaccination: a coverage of < = 50% in 6-month-olds could half total incidence in the 10 years following vaccine in low transmission intensity settings, even if effects only lasted 1 year. In a moderate transmission intensity setting, between 50–70% coverage was needed, and in high transmission intensity settings 70–90% coverage, depending on duration of vaccine induced effects. Across all transmission intensities, assuming the vaccine reduced susceptibility of vaccinated animals to 50% or 75% (efficacy scenario 2) only afforded a very small (~ 1% on average) additional reduction in incidence compared to when assuming the vaccine reduced infectiousness alone ( Figure S8 ). In the population of 2 million, when R 0 was low, coverage was high, and the effects of the vaccine were long-lasting, vaccination was capable of interrupting transmission and led to stochastic fadeout. In these cases, the difference in incidence between stochastic runs was often large (the 2.5% and 97.5% quantiles are represented by transparent ribbons in Fig. 4 B). In low and moderate intensity settings, vaccination was able to interrupt transmission when coverage in 6-month-olds was very high and the vaccine induced effects lasted at least 3 years (Table 3 ). In high transmission intensity settings transmission was only interrupted when coverage was 100% and vaccine induced effects lasted 10 years. In the smaller population of 75,000 divided into homogenous patches of 3,000, stochastic fadeout occurred at lower coverages and across a wider range of scenarios. Vaccination was capable of reliably interrupting transmission when coverage ranged from 40–80% depending on transmission intensity and duration of vaccine induced effects. Table 3 The coverage necessary to interrupt transmission at the population level in two modelled populations Vaccine coverage (%) needed to interrupt transmission in a population of : R 0 1/ρ 75,000 split into patches of 3,000 2,000,000 split into patches of 80,000 3.5 1 40 NA 3 40 90 10 40 70 7.0 1 80 NA 3 60 100 10 60 90 14.0 1 80 NA 3 70 NA 10 60 100 Discussion Understanding the transmission dynamics of MERS-CoV in camels is vital to evaluating the potential public health impacts of animal vaccination but has been hindered by the scarcity of data describing what is largely an asymptomatic infection in this species. By using age-stratified seroprevalence and viral load data extracted from published studies, we estimated the transmissibility of MERS-CoV in camels and developed a dynamic model of transmission, allowing for the first evaluations of the potential impact of camel vaccination under different efficacy scenarios. Whilst considerable uncertainty around immunity and aspects of vaccine efficacy remains, we have gained several insights into the transmission dynamics and controllability of MERS-CoV in camels. The transmissibility of MERS-CoV was generally estimated to be higher in camel populations in the Middle East compared to those sampled in South Asia and Africa. All viruses sampled from camels in Africa have been classified into Clade C based on their genetic similarities. Strikingly, despite the large number of live camels imported from Africa, all viruses isolated from camels and humans in the Arabian Peninsula have belonged to genetically distinct clade A and B viruses – even those isolated from newly imported animals 31 . Our estimates of higher MERS-CoV transmissibility in camels in the Middle East align with the results of a recent study that found clade C to have a reproductive disadvantage compared with clade A and B in human lung tissue 32 , suggesting that the clade C viruses prevalent in Africa may be intrinsically less transmissible to humans, and perhaps between camels as well. However, by underpinning interactions between susceptible and infectious animals, variation in global camel husbandry practices could also potentially explain differences in transmissibility. The camel population is highly heterogeneous in terms of husbandry practice even at the local scale 33 , 34 . In the Arabian Peninsula, camel farming has become increasingly intensive and urban in the past 60 years, whilst remaining largely extensive pastoralist techniques elsewhere 35 . Further investigation of what is driving perceived differences between transmission intensities will be important for devising context-specific vaccination or other control strategies. It is important to highlight several limitations that could also have affected our transmissibility estimates. Firstly, the camel populations reflected in the FoI and R 0 estimates are based on age stratified seroprevalence surveys and are likely to be affected by several biases including bias towards countries with sufficient resources to detect human cases and to study MERS-CoV in their camel populations. It was not possible to estimate the transmissibility of MERS-CoV in Somalia and Sudan – the most camel dense areas in the world – due to a lack of age stratified seroprevalence data for these populations. Secondly, surveys used different tests to determine seropositivity, likely with different sensitivity and specificity. Since the FoI is estimated using relative differences in seroprevalence within a single study where a single test type was used, our estimates should not be greatly affected except when seroprevalence is very high as it may approach the limit of sensitivity. To test the influence of differences in test sensitivity and specificity across test types, models were re-fit to the data whilst assuming reduced specificity of ELISAs and reduced sensitivity of neutralisation tests. Our ranking of transmissibility, with generally higher estimates in the Middle East and lower in Africa and South Asia, was robust to this change. Finally, the rate of seroreversion could not be reliably identified due to uncertainty around test sensitivity and challenges distinguishing long-lasting antibodies from repeated boosting of antibodies following recurring infection in the catalytic model framework. Documented reinfection of seropositive animals shows that antibodies are not a proxy for complete immunity to MERS-CoV. With this in mind, the rate of seroreversion was not used to inform parameterisation of waning immunity in the transmission model, instead several alternative values were used, meaning this limitation did not impinge the vaccine impact modelling. Our estimates of the CCS were larger than most reported herd sizes which tend to be well under 1,000 animals 33 , 34 , 36 , emphasising the importance of focusing interventions on reducing inter-herd infections for interrupting transmission. The dependence of the CCS on transmissibility, together with the difference in R 0 across populations, suggests that MERS-CoV may be able to persist in a population 2–20 times smaller in high transmission settings found in the Arabian Peninsula, compared to lower transmission settings. The results of our simulations suggest that seasonality of births can be expected to drive annual, seasonal peaks in infection in large populations when transmission intensity is comparable to the high R 0 estimates for MERS-CoV in populations in parts of the Middle East, and Ethiopia. Whilst infections tended to peak outside of the calving season, the lag between the simulated peak in births and annual peak in infection depended on R 0 . If MERS-CoV infections peak annually in some settings as these simulations suggest, there may be seasons in which risk of zoonotic transmission is elevated. Better understanding of when peaks occur would provide opportunities to mitigate risk and avert human cases. Although we considered seasonal calving, other factors such as annual migrations and events that affect camel mixing could also affect the transmission dynamics of MERS-CoV in the zoonotic reservoir. Ultimately, it is necessary to undertake long-term surveillance over several years to better ascertain the seasonality of MERS-CoV in camels. Our vaccination simulations indicated that if a MERS-CoV vaccine is able to reduce infectiousness in naïve and previously infected camels, large reductions in incidence are possible, provided that a high proportion of calves are vaccinated. Little impact was seen if the vaccine was only effective in previously infected animals. Although the ChAdOx1 MERS vaccine was measured to have poor efficacy in naïve animals in an initial field trial, potentially due to the animals’ age 6 , the MVA vaccine has been shown to reduce shedding in naïve animals 7 . Assuming independence of efficacy on age, we saw that vaccinating calves in their first few months of life maximises reductions in overall incidence among camels. Our observation that under some scenarios vaccination of very young calves led to more infections in adult animals, highlights the importance of understanding the age dependency of human-camel contact patterns across different populations. Vaccination strategies should be evaluated not only on their likely impact on transmission amongst camels, but also on the age distribution of infections in light of such contact patterns. When simulated vaccine coverage was high, vaccination led to large reductions in infection incidence even when the vaccine was only assumed to reduce infectiousness rather than susceptibility. When infectiousness was assumed to be proportional to viral RNA shedding, the coverage needed to interrupt transmission in a population of 2 million was > 70% across all scenarios, reaching 90–100% in moderate-high transmission intensity settings. This suggests that, in a large population with extremely high levels of camel mixing it would be difficult to entirely interrupt transmission in some Middle Eastern settings by using vaccination of calves alone, but that incidence could still be greatly reduced. Our estimates are limited by the absence of data on true population structures and movement. Instead, a rudimentary grid of connected sub-populations was used to approximate the structuring of the population into herds or patches. In smaller or more fragmented populations with less mixing interruption could likely be achieved with a lower coverage. Tailoring the model to specific populations using camel herd size, contact and movement data would be necessary to better evaluate the likely impact of vaccination in populations of most interest to decision makers. Two additional data gaps limited our assessment of vaccine impact. Firstly, the undefined relationship between viral RNA shedding data and infectiousness. If rather than being proportional to viral shedding, the relationship is closer to being logarithmic, meaning vaccination leads to a much smaller decrease in infectiousness, then our results suggest that large reductions in incidence would be much more difficult to achieve and require even higher vaccine coverage. Secondly, the effect of vaccination on susceptibility and vaccine effectiveness in naïve animals were not clear from current field trials so we included a range of scenarios as sensitivity analyses. As these aspects of MERS-CoV transmission and vaccination in camels become better characterised, it will be possible to improve mathematical models of MERS-CoV transmission and become increasingly confident that they accurately represent transmission in the zoonotic reservoir. Models of MERS-CoV transmission have previously focused on human-to-human transmission. However, as recurring camel-to-human transmission drives human cases there was a growing need for a model of transmission in the zoonotic reservoir. The model presented here provides a framework in which to simulate MERS-CoV vaccination strategies in camels which, together with improved data on camel mixing patterns and further empirical studies of vaccine efficacy, could offer an important contribution to inform effective responses to the zoonotic transmission of MERS-CoV. Efforts to better define the relationship between the number of infectious camels and the risk of zoonotic spillover events would allow the expected reduction in infection amongst camels to be translated into the expected number of human cases averted, permitting evaluation of the cost-effectiveness of camel vaccination as an intervention against human cases of MERS-CoV. Methods Further details of methods are given in the Supplementary Materials. Estimating the transmissibility of MERS-CoV in camels We estimated two different measures of the transmissibility of MERS-CoV in camels: the Force of Infection (FoI, l) defined as the rate at which susceptible animals become infected, and the reproduction number (R 0 ) defined as mean number of individuals infected by a single infected individual in an entirely susceptible population. FoI We fitted catalytic models of seroconversion to age-stratified seroprevalence estimates from across Africa, South Asia and the Middle East collated previously through a systematic review 8 . Since the catalytic modelling approach assumes seroprevalence estimates are derived from a random cross-sectional sample of individuals, we excluded 3 of the 19 reviewed studies based on their sampling strategies (please see Supplementary Materials for more details on study inclusion). To make the geographical range of the FoI estimates as comprehensive as possible, we used seroprevalence measures from one additional study published after the systematic review. This allowed us to include camel populations in Senegal and Uganda which were not previously represented in the literature 16 . The tests used to determine seropositivity varied between studies and included both neutralisation tests (NTs) and non-neutralising Enzyme Linked Immunosorbent Assays (ELISAs) ( Table 1 ) . NTs are shown to be highly specific to MERS-CoV antibodies with little cross-reactivity with other camel coronaviruses 26 , 37 – 39 . MERS-CoV IgG ELISAs have been measured to be 99% specific when correlated with NTs 28 , 40 . Whilst ELISAs are considered more sensitive than NTs as they can pick up non-neutralising antibodies 41 , the seroprevalence measured by studies using NTs often approaches or reaches 100% in adult camels suggesting that – assuming they are indeed highly specific – they must also be highly sensitive. Therefore, we assumed a high sensitivity (98% for both test types) and specificity (99.5% for NTs and 98.5% for non-NTs) in our core results. We then conducted a sensitivity analysis assuming NTs to have a lower sensitivity of 85%. We compared the fit of four models of seroconversion. In model 1 we assumed that all animals are born seronegative and become seropositive at a constant rate l, as originally conceptualised by Muench and now regularly applied to epidemiological data 42 , 43 . Since MERS-CoV reinfection has been documented in camels 6 , 13 , 14 , in model 2 we extended model 1 to allow for seroreversion - with protective antibodies waning at rate s. In model 3 we extended model 1 to allow a proportion of calves to be born seropositive due to protective mAbs which wane at rate w, as evidence suggests that calves born to seropositive mothers are shown to acquire MERS-CoV specific mAbs through colostrum 11 , 12 . Finally, our fourth model allowed for both mAbs and seroreversion. Please see the Supplementary Materials for equations describing the solutions used for each model. We fit the models within a Bayesian framework using Hamiltonian Monte Carlo (HMC) sampling algorithm implemented in the R software package rstan 44 . Whilst we estimated the FoI per study to account for potential true differences between the FoI across husbandry systems, we assumed antibody waning rates to be constant, estimating them globally across all the datasets. We assumed that the seroprevalence data was beta-binomially distributed and re-parameterised the beta-binomial distribution in terms of the mean probability of being seropositive and the overdispersion parameter k where k > 0 and a k approaching zero would indicate negligible overdispersion. A detailed reparameterization available in the Supplementary Material . In order to evaluate which of the models was best supported by the data, we compared their fit using the Deviance Information Criterion 45 . The reproduction number (R) We estimated R 0 of MERS-CoV in each study population by calibrating a dynamic model of MERS-CoV transmission (see next section) to the modal FoI, by varying the transmission intensity parameter, b, under different potential immunity scenarios. R 0 was approximated as the product of b and the infectious period, g. The one-to-one relationship between b and the FoI meant that the credible intervals (CrIs) around the FoI could be used to propagate the uncertainty into the R0 estimates. Development of a dynamic model of MERS-CoV transmission in camels Infection Based on what we know about camel demography from the literature, and our estimates of transmissibility and maternal antibody waning, we developed a stochastic, age-structured model of MERS-CoV transmission in camels. The model structure is represented schematically in Fig. 5 , with a single age class shown for clarity. All symbols used are defined in Table 4 alongside the parameter values and their sources. Camels are born either entirely susceptible to MERS-CoV infection (state S 1 ) or with complete protection by mAbs (state M ) which wanes at a rate ω with calves becoming susceptible after an exponentially distributed period with a mean of ~ 2 months as estimated from the age-stratified seroprevalence data. The proportion of calves born in state M is dictated by the proportion of animals of reproductive age (> 4 years) which have been previously infected. Animals in S 2 become infected and transition to state I 1 with the FoI, λ 1 , defined as the product of the effective contact rate, β , and the proportion of individuals in the population which are infectious: $$\:\begin{array}{c}{\lambda\:}_{1}=\beta\:\frac{{I}_{1}+r{I}_{2}}{N}\: \:1\end{array}$$ where I 2 is the number of reinfected individuals, r < 1 and represents the relative infectiousness of reinfections compared to first infections, and β is varied to calibrate λ 1 to our FoI estimates from age-stratified seroprevalence data. No data is available on the potential latent period following MERS-CoV infection in camels. Infected animals are assumed to be instantaneously infectious. The period spent in state I 1 is exponentially distributed around a mean of 14 days in agreement with the duration of shedding reported in longitudinal studies 6 , 11 , 13 . Table 4 Transmission model parameters Description Values Source N 0 Initial population size Varied (50 − 10,000,000) NA 𝛼̅ Mean birth rate Varied annually around a mean of 0.000565 camel − 1 day − 1 based on initial population size. Estimates of 45.2% annual fecundity in KSA 46 taken together with assumptions that 90% of the population are female due to high male removal rate 33 and that 50% of the female population are of reproductive age 33 β Effective contact rate 0.1-1.0 camel − 1 day − 1 Calibrated to our FoI estimates from age-stratified seroprevalence data γ Rate of recovery from infection 1/14 days δ Strength of seasonality of births 1 (0, 0.5 also considered) 33 see “Births” in Methods . σ Rate of waning immunity following infection 1/30 days, 1/90 days (scenario with no complete immunity also considered) NA λ 1 Rate at which susceptible animals become infected, equal to \(\:\beta\:\frac{{I}_{1}+r{I}_{2}}{N}\) 0.1-3.0 calibrated by varying β Our estimates from age-stratified seroprevalence data ω Rate of waning of mAbs 0.0136 day − 1 Our estimates from age-stratified seroprevalence data µ 1 Daily mortality rate of camels 2yrs 0.00036 camel − 1 day − 1 Within the ranges described in 46 but exact value set to balance mean birth-rate φ Susceptibility to reinfection relative to first infection 0.75 (0–1 considered) NA r Infectiousness of reinfections relative to first infections 0.01, 0.50 6 see “Immunity” in Methods . Immunity Whilst our inference from age-stratified seroprevalence suggests that under catalytic model assumptions antibodies may be long-lasting following infection, documented reinfection of seropositive animals and rapid reinfection in high transmission intensity environments indicates that MERS-CoV seropositivity is not a good proxy for protective immunity in camels 6 , 13 , 14 . We therefore explored multiple reinfection scenarios (Table 4 ). Following a short period of complete immunity in state R , individuals become susceptible to reinfection in state S 2 . Most animals found to be shedding MERS-CoV in field surveys are calves and naïve animals, suggesting there is some long-term protection offered by past infection 24 , 25 , 34 , 47 . To reflect this, the degree of susceptibility in state S 2 is modelled to be less than that experienced by individuals in state S 1 , meaning individuals in state S 2 experience a reduced FoI, λ 2 . Reinfected individuals in I 2 . are modelled to be less infectious than individuals in I 1 . This is based on measures of viral load collected in the control arm of the ChadOx1 MERS vaccine field study in camels 6 . We digitally extracted the daily mean viral load for seronegative calves (which we assumed to be infected for the first time during the study) and seropositive calves (which we assumed to be reinfected during the study) in the unvaccinated control group in from Fig. 4 A of the online publication using PlotDigitizer version 2.2 48 . We then calculated the difference between the area under the viral load curve for each of the two groups. Reinfected animals were approximately 1% as infectious as first-time infected animals when assuming a linear relationship between viral load and infectiousness. The relationship between viral load and infectiousness is not well characterised. A trial of the MVA-based vaccine candidate in camels measured a similar decrease in a measure of infectious viral particles and a measure of viral RNA shedding, following vaccination of four calves 7 . However, the study was not designed to have the power to reliably define the relationship between infectious virus particles and infectiousness. Therefore, whilst our core results assume a linear relationship, with reinfected individuals 1% as infectious as first-time infections, we include a sensitivity analysis assuming that the relationship between viral load and infectiousness is logarithmic, with a relative infectiousness of 50% for reinfected animals. Age structure Inclusion of age structure is vital given the strong dependence of infection status and seroprevalence on age, as well as for simulating age targeted interventions. Fine age structure is especially important up until the age of four years to enable accurate representation of age within the window where first infections are happening and accurate, age-targeted intervention modelling. For this reason, the model is stratified into month-wide classes, with camels moving to the next age-strata every 30 days in a 360-day year. From the 48th month wide class, camels enter a class aged > 4 years where they remain until death. Births Camel calving is reported to be strongly seasonal 33 , 49 – 53 . Studies in KSA report most calves being born between October and March, with one study quantifying this at 83% during the high season 33 . The calving season is very similar in Egypt where it is reported between October and April 53 and in Nigeria where surveyed pastoralists identified the calving peak to occur in the early dry season between October to December 49 . To capture this seasonality, the number of births per day is drawn from a Poisson distribution with a mean of αΝ 0 where α varies annually as a function of cosine (Equations 2&3) and N 0 is the initial population size. The strength of seasonality can be weakened by setting δ < 1 during sensitivity analyses. However, when δ = 1, 82% of births fall between October and March which is in line with the 83% reported for camel births in Qassim, KSA 33 . $$\:\begin{array}{c}births\:\sim\:Pois\left(\alpha\:\left(t\right)*{N}_{0}\right) 2\end{array}$$ $$\:\begin{array}{c}\alpha\:\left(t\right)=\stackrel{-}{\alpha\:\left\{1+\text{cos}\left(\frac{2\pi\:t}{360}\right)\right\}}\: 3\end{array}$$ Deaths Since MERS-CoV causes very mild disease in camels, infection is modelled to have no bearing on mortality. Camels die off from each disease state compartment at the same age dependent average rate µi, with the mean number of deaths per day being equal to the size of the compartment multiplied by 1 – e -µ i . The model assumes a higher probability of calf death in the first two years of life than in adulthood, as reported in KSA 33 , 46 . The modelled mortality rates are calibrated to the birth rate to give a stable population size and are equivalent to ~ 40% mortality in the first two years of life and ~ 12% afterwards, similar to overall mortality estimates for populations in KSA which are described in the literature as 10–26%, depending on herd type 46 . Structure For large populations, it becomes unrealistic to assume populations are well mixed. For example, in the population of ~ 10,000 camels in Laikipia county, Kenya 54 , an individual camel is far more likely to have contact with individuals in its own herd or grazing area than with animals in other areas of the county. The movements and interactions between herds of camels are not well documented. To explore the effect population structure has on dynamics, we developed a rudimentary structured population model where sub-populations or patches are arranged over a grid ( Figure S5 ). Individuals are most likely to be in contact with other individuals in the same patch, less likely to meet individuals in neighbouring patches, and do not meet individuals in distant patches. Until better data on population structure allows a more accurate representation of networks and movements of camels within a region, the grid serves as a naïve representation of this reality. We coded the model in R 55 version 3.5.3, using the package odin 56 and ran stochastic iterations using odin.dust 57 , 58 . Estimating the Critical Community Size (CCS) To evaluate the CCS of MERS-CoV in camel populations, we estimated the size of the population required for transmission to be sustained for at least 25 years in a closed population with no external sources of infection. The CCS was defined as the population size at which transmission was sustained in at least 50% of stochastic model runs. We ran the model using population sizes ranging from 500-1000000 and estimated the precise population at which 50% persistence was achieved using linear interpolation. Evaluating the periodicity of infections To determine the average time between peaks in infections we estimated the autocorrelation between each simulated time series of infections and lagged versions of itself using Pearson’s correlation test implemented through the acf function in the R “stats” package. The lag that maximised the autocorrelation coefficient was used to estimate the periodicity, for example if the lag that maximised the autocorrelation coefficient was between 350–370 periodicity was classified as annual. Very short lags of < 100 days and any acf below the significance level using 95% confidence interval (CI) were excluded. Estimating vaccine impact We extended the transmission model to simulate vaccination by duplicating the set of disease states to create a parallel set of vaccinated states. Although two vaccine candidates have been shown to reduce viral shedding in camels, uncertainty remains around their ability to reduce susceptibility and around the effectiveness of the ChAdOx1 MERS vaccine in naïve animals. Due to these uncertainties, three main scenarios are modelled (Fig. 6 ). In our core scenario 1, the vaccine reduces infectiousness but not susceptibility to infection for all vaccinated animals. This scenario reflects the finding that all previously naïve vaccinated animals became infected when challenged. Challenge doses administered intranasally or by confinement with multiple infectious animals could be much higher than the average natural exposure, and 1/5 of the previously infected ChAdox1 vaccinated animal did not become infected despite challenge so we also explored an alternative scenario 2 in which the vaccine reduces both infectiousness and susceptibility for all vaccinated animals. Finally, we explored a third scenario in which the vaccine reduces both infectiousness and susceptibility but only in animals that have been previously infected. Although the MVA study measured a large reduction in infectiousness of previously seronegative vaccinated animals, the ChadOx1 vaccine was only measured to reduce shedding in previously infected vaccinated animals. Authors suggest the low efficacy in this group could be due to the naive animals being very young, but their age was comparable with those used in the MVA study. Parameters used in vaccination simulation are presented in Table S5 . Due to the scope of the efficacy studies, it is not possible to estimate the rate of waning of vaccine-induced effects, 1/ρ. Instead, for each main scenario, three options are explored with effects lasting one, three and ten years. The relative infectiousness of vaccinated infected animals and of vaccinated reinfected animals compared to unvaccinated naïve animals was parameterised using viral RNA shedding data 6 , assuming that infectiousness is either proportional to viral RNA shedding or to the log of viral RNA shedding. Vaccination is implemented in an age dependent manner and occurs immediately at the point at which camels reach the age being targeted for vaccination. To evaluate the ideal age for vaccination under the model assumptions, the target age group was varied from one month old to four years old. The vaccine efficacy was not modelled to vary with age. In scenarios 1 and 2 vaccination was assumed to reduce the relative infectiousness of first-time infected animals ( r v ) by the same amount as natural infection reduces viral shedding in reinfected individuals. The relative infectiousness of reinfected vaccinated individuals ( r inf_v ) was estimated as 0.15% when a linear relationship between infectiousness and viral RNA shedding was assumed, and 33% when infectiousness was assumed to be proportional to the log of viral RNA shedding 6 . See Table 5 for the complete set of parameters used to model the effect of vaccination. Vaccine impact was measured as difference in incidence following annual vaccination over a ten-year period and potential to disrupt patch or population-level transmission. Table 5 Parameters used to simulate vaccination under different efficacy scenarios Description Values Source r v Relative infectiousness of vaccinated animals 1% (50% as sensitivity analysis assuming infectiousness is proportional to the logarithm of viral load). 6 , 7 r inf_v Relative infectiousness of vaccinated previously infected animals 0.15% (33% as sensitivity analysis assuming infectiousness is proportional to the logarithm of viral load). 6 φ v Relative susceptibility of vaccinated animals 100% (or 75% in scenario 2) NA. Larger reduction in susceptibility unlikely given all vaccinated previously seronegative animals were infected in 6 , 7 . φ inf_v Relative susceptibility of vaccinated previously infected animals 75% (or 75% in scenario 2) NA. Indication of reduced susceptibility 6 but unable to reliably quantify in small population 1/ρ The rate of waning of vaccine-induced effects 1, 3 or 10 years − 1 Declarations Competing Interests The authors declare no competing interests. Funding Statement Amy Dighe acknowledges funding from the Wellcome Trust Studentship 203871/Z/16/Z. All authors acknowledge funding from the Medical Research Council (MRC) Centre for Global Infectious Disease Analysis (MR/X020258/1) funded by the UK MRC and carried out in the frame of the Global Health EDCTP3 Joint Undertaking supported by the EU; the NIHR for support for the Health Research Protection Unit in Modelling and Health Economics, a partnership between the UK Health Security Agency (UKHSA), Imperial College London, and London School of Hygiene & Tropical Medicine (grant code NIHR200908); a philanthropic donation from Community Jameel supporting the work of the Jameel Institute. The funders had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. For the purpose of open access, the author has applied a ‘Creative Commons Attribution’ (CC BY) licence to any Author Accepted Manuscript version arising from this submission. Author contributions Conceptualisation: NF, AD, and TJ, formal analysis: AD, methodology: NF, AD and TJ, manuscript writing – original draft AD, writing - review and editing NF and TJ. Acknowledgements The authors acknowledge helpful input from Maria Van Kerkhove in shaping the focus of this work. Data and code availability Data and code used to generate the results described in this paper are available here: https://github.com/AmyDighe/mers-cov-camels . References Lessler J et al (2016) Estimating the Severity and Subclinical Burden of Middle East Respiratory Syndrome Coronavirus Infection in the Kingdom of Saudi Arabia. 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R: A Language and Environment for Statistical Computing (2021) FitzJohn R, Fischer T (2022) odin: ODE generation and Integration FitzJohn R, Lees J (2022) odin.dust: Compile Odin to Dust Lees JA et al (2021) Reproducible parallel inference and simulation of stochastic state space models using odin, dust, and mcstate. Wellcome Open Res 5 Additional Declarations There is NO Competing Interest. Supplementary Files SMdraft161024.docx Cite Share Download PDF Status: Published Journal Publication published 18 Aug, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5342913","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":372615709,"identity":"91c01d53-7713-45ed-aae2-dca9ac725dbe","order_by":0,"name":"Amy Dighe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYJACiQSGAwz8IFZCASlaJBtAWgyI1cIA1GJwAMQkRgv/tDOGNx78uiNnfH514ocHBgzy/GIHCNhwO8fYIrHvmbHZjbebJYAOM5w5O4GANbdzzCQSew4nbrtxdgNIS4LBbQJa5KFa6jfPOLv5B1FaDEBaEn4cTjDg791GnC2Gt9OKLRIbDhvOuMG7zSLBQIKwX+RuJ2+8+ePPYXn+/rObb/6osJHnlyaghYGBw4CBsY0BHKEM4DgiDNgfMDD8AdL8B4hRPQpGwSgYBSMRAABP/UzrqLcSAAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-5024-8061","institution":"Imperial College London","correspondingAuthor":true,"prefix":"","firstName":"Amy","middleName":"","lastName":"Dighe","suffix":""},{"id":372615710,"identity":"beb85805-3866-4c11-a0e6-f7f6d2e0e329","order_by":1,"name":"Thibaut Jombart","email":"","orcid":"","institution":"LSHTM","correspondingAuthor":false,"prefix":"","firstName":"Thibaut","middleName":"","lastName":"Jombart","suffix":""},{"id":372615711,"identity":"fa55b091-a4a1-451a-abe4-723b63c55ec4","order_by":2,"name":"Neil Ferguson","email":"","orcid":"https://orcid.org/0000-0002-1154-8093","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Neil","middleName":"","lastName":"Ferguson","suffix":""}],"badges":[],"createdAt":"2024-10-27 22:05:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5342913/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5342913/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-025-62365-x","type":"published","date":"2025-08-18T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68140300,"identity":"f0853692-ccae-47c3-9650-51ba1afc4731","added_by":"auto","created_at":"2024-11-04 04:45:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":286138,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePredicted seroprevalence and FoI estimates using Model 4. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eA.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Posterior estimates for the FoI for each study, where the point represents the mode and the line represents the 95% CrI\u003c/em\u003e. \u003cem\u003e\u003cstrong\u003eB.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Predicted seroprevalence by age (blue) with vertical 95% CrI, fitted to age-stratified seroprevalence shown with vertical 95% CI (red). Horizontal bars show age class width.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5342913/v1/8d86988f1c8c90b485160a9c.png"},{"id":68140301,"identity":"e59e28b6-8c3d-4c47-8a01-5e21be25c199","added_by":"auto","created_at":"2024-11-04 04:45:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":169830,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe percentage of stochastic model runs in which transmission persists by population size under different transmission intensities (line colour), and population structure assumptions (homogeneous = solid lines and structured population = dashed lines). Panels separate scenarios with differing strength of seasonality of births (columns), and relative infectiousness of reinfected individuals (rows). The horizontal dashed line indicates persistence in 50% of model runs – the point at which persistence becomes more likely than fadeout.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5342913/v1/418f952dcb82b4186097d7c7.png"},{"id":68140464,"identity":"8915f7a7-0895-40be-aec4-d6cb8efc1843","added_by":"auto","created_at":"2024-11-04 04:53:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":361210,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. Examples of simulated epidemic curves under three sets of conditions in which infections peak annually, shown in relation to the calving season (shown shaded in blue, with simulated number of births over the course of a year under different strengths of seasonality represented as dotted lines). A large population size was modelled for clarity. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Examples of stochastic epidemic curves with biennial (blue) and triennial (green) periodicity arising in smaller populations, with a single time series of each type highlighted in bold.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5342913/v1/9fe6a30680b0cfdc8868c260.png"},{"id":68140304,"identity":"e32e5c89-2b6b-4dee-ba47-cdf04afc0d44","added_by":"auto","created_at":"2024-11-04 04:45:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":169894,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eA. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eThe percentage reduction in incidence of MERS-CoV infection in camels depending on age group targeted, in different transmission settings (rows), for varying durations of vaccine effects (colours). Transparent ribbons show the 2.5-97.5% quantiles, with stochastic fadeout of transmission leading to larger differences across stochastic runs in some scenarios. The red dashed line indicates the 6-month age class. Vaccine coverage was assumed to be 80% in the target age class. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e the percentage reduction in incidence of MERS-CoV infection in the 10 years following introduction of vaccination of 6-month-old calves, in camel populations of 2 million (comparable to that of KSA) made up of 25 homogenous patches of 80,000 animals connected to their nearest patches.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5342913/v1/4187ce1e186a4fbb47bba086.png"},{"id":68140302,"identity":"6f9ab4c8-f0fd-47d7-9488-17a3551fdca1","added_by":"auto","created_at":"2024-11-04 04:45:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":23190,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eA dynamic model of MERS-CoV transmission in camel populations shown for the youngest of i age classes (i = 1). The states represented are defined as follows M - maternally acquired immunity, S\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e - susceptible, I\u003c/em\u003e\u003csub\u003e\u003cem\u003e1 \u003c/em\u003e\u003c/sub\u003e\u003cem\u003e- first-time infected, R - recovered, S\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e – susceptible again following infection and I\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e - reinfected. This structure is repeated for each of the further 48 age classes. The symbols used to represent rates are defined in Table 4.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5342913/v1/65fafe8c7c3ebcc49b8c96cb.png"},{"id":68140466,"identity":"2697df38-76af-4b98-b8aa-5d267b324fa2","added_by":"auto","created_at":"2024-11-04 04:53:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":44054,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eModelled vaccine efficacy scenarios.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-5342913/v1/86ecff79c929cb0a839f8355.png"},{"id":89354302,"identity":"5f032c9e-98a9-4a24-a744-078b3a78c534","added_by":"auto","created_at":"2025-08-19 07:07:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1855937,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5342913/v1/50593468-4253-4681-b923-6084df62ede9.pdf"},{"id":68140303,"identity":"9f108aa8-eb8c-4b4d-a8de-60543ada7782","added_by":"auto","created_at":"2024-11-04 04:45:20","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1376502,"visible":true,"origin":"","legend":"","description":"","filename":"SMdraft161024.docx","url":"https://assets-eu.researchsquare.com/files/rs-5342913/v1/4afb716feb97592dfa609600.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Modelling transmission of Middle East respiratory syndrome coronavirus in camel populations and the potential impact of animal vaccination","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMiddle East respiratory syndrome coronavirus (MERS-CoV) causes severe acute respiratory disease in humans, with an estimated infection fatality ratio of approximately 22%\u003csup\u003e1\u003c/sup\u003e. Although capable of spreading rapidly in hospital settings\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, MERS-CoV transmission is inefficient in the general community and recurrent outbreaks in humans are driven by repeated zoonotic spillover from dromedary camels (\u003cem\u003eCamelus dromedarius\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e from here on referred to as \u0026ldquo;camels\u0026rdquo;. The role of camels in ongoing transmission has led to demand for camel vaccination as part of a set of interventions to avert human cases\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. With two vaccine candidates shown to reduce viral shedding\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, there is a need to assess the potential impact of camel vaccination on transmission. This is hindered, however, by poor understanding of the epidemiology of MERS-CoV amongst camels and a lack of mathematical models of transmission dynamics in the animals.\u003c/p\u003e \u003cp\u003eMost camels show no outward signs of MERS-CoV infection, making assessing the epidemiology of the virus in the zoonotic reservoir challenging. Cross-sectional surveys testing for antibodies against MERS-CoV or viral RNA have demonstrated that as well as being endemic in camel populations in the Middle East where autochthonous human MERS cases are reported, the virus is present in camels in parts of South Asia and circulates widely across Africa where the majority of the world\u0026rsquo;s camels reside\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Little is known about transmission intensity and how this varies within the global camel population which is highly heterogeneous in terms of structure and husbandry practices. Only one study has estimated the annual force of infection, estimating it to be 0.4 in ranch populations and 0.1-1.0 in pastoral populations in Kenya\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Knowledge of immunity is also limited, but longitudinal studies have provided two important insights. Firstly, studies in a small number of mother-calf pairs observed a wave of infection sweeping through calf populations after maternally-acquired antibodies (mAbs) waned over the first few months of life, suggesting protective mAbs may play a role in infection dynamics\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Secondly, studies have demonstrated reinfection of previously seropositive animals, and rapid reinfection of infected animals in high density settings such as markets and holding pens\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Unfortunately, longitudinal studies have been too short or small to reliably measure how immunity and calving might lead to seasonal variation in infection. Evidence from phylogenetic analysis suggests the risk of spillover is highest between April and July\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, but this is not reflected by the incidence of primary cases\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. These knowledge gaps around transmission intensity and immunity, taken together with the lack of mathematical models of transmission in camels, impede the design of informed animal vaccination strategies, and the evaluation of their potential impact.\u003c/p\u003e \u003cp\u003eHere, we use published age-stratified seroprevalence data from camel populations across Africa, the Middle East, and South Asia to fit catalytic models of seroconversion and produce population specific estimates of MERS-CoV transmissibility in camels. We then introduce a stochastic, age-structured, dynamic transmission model of MERS-CoV in camels, which we use to estimate key, epidemiological quantities, including \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e, the Critical Community Size (CCS) and periodicity of infections, thereby providing insights into how controllable MERS-CoV may be in different camel populations. Finally, we use our model to simulate vaccination assuming different efficacy scenarios. We evaluate the potential impact of vaccination on transmission in camels, as well as the optimal age for vaccination. Alongside empirical studies, insights from dynamic models such as those developed here could contribute to informing an effective response to the zoonotic transmission of MERS-CoV.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTransmissibility of MERS-CoV in camel populations\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eForce of infection (FoI)\u003c/h2\u003e\n \u003cp\u003eTo estimate the FoI, we fitted four different models of seroconversion to age-stratified seroprevalence data extracted in our previous systematic review of MERS-CoV in camels\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, assuming the seroprevalence data was beta-binomially distributed. We found that allowing a proportion of calves to be born with protective maternally acquired antibodies (mAbs) in model 3 improved model fit, as did the inclusion of seroreversion due to waning of antibodies acquired following infection in model 2, though the additional improvement in fit afforded by seroreversion on top of mAbs in the best fitting model (model 4) was fairly small (\u003cem\u003eTable \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/em\u003e). We assumed test sensitivity and specificity were high for both neutralisation and non-neutralisation-based tests. The ranking of model fit was robust to the use of alternative assumptions where sensitivity of neutralisation tests was modelled to be lower at 85% (\u003cem\u003eTable S2\u003c/em\u003e). Model selection was also generally robust to exclusion of each dataset from the analysis, with the model with mAbs alone or with mAbs and seroreversion outperforming the others \u003cem\u003e(Figure S2).\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eParameters governing rates of antibody waning and data overdispersion were estimated as common to all studies, while the FoI was allowed to be study-specific. We estimated that mAbs waned rapidly in the first few months of life, lasting on average 2 months (95% credible interval (CrI): 1\u0026ndash;4 months) in our best fitting model, but that antibodies wane slowly following infection, lasting approximately 17 years (95% CrI: 9\u0026ndash;33 years) \u0026ndash; approaching the lifespan of camels. However, it can be difficult to distinguish life-long antibodies from repeated boosting using catalytic models. Parameter estimates were largely robust to exclusion of each data set with the exception of the data collected in Egypt which, when excluded, increased the estimated duration of mAbs to 4.8 months, similar to the 4.2 months estimated in model 3 (\u003cem\u003eFigure S3\u003c/em\u003e). The overdispersion parameter, \u003cem\u003ek\u003c/em\u003e, was estimated to be 2.5 (95% CrI: 2.0, 3.2), meaning the variance of the data was estimated to be around 3.5 times greater than what would be expected if the data were binomially distributed. When an uninformative prior for \u003cem\u003ek\u003c/em\u003e was used, the model tended to maximise \u003cem\u003ek\u003c/em\u003e meaning an extreme level of overdispersion could on its own account for the patterns observed in the data, irrespective of other epidemiological parameter values which were then unidentifiable. To circumvent this issue, a half normal prior with a standard deviation of 0.5 was used to constrain \u003cem\u003ek\u003c/em\u003e to values we believe to be more plausible.\u003c/p\u003e\n \u003cp\u003eThe annual FoI of MERS-CoV in camels was generally higher in populations sampled in the Middle East, and lower in those sampled in South Asia and Africa (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). This trend was consistent across all four models (\u003cem\u003eTable S3).\u003c/em\u003e The posterior mode for the FoI ranged from 0.1-3.0 across most study populations, except for in the population in UAE where seroprevalence was very high (\u0026gt;\u0026thinsp;85%) in both calves and adults, perhaps indicative of a recent outbreak, and the FoI was estimated to be 7.1/year. Such high FoI estimates are necessary to explain the high seroprevalence measured in younger animals when assuming endemic transmission, but it is important to note that there is a risk of over-estimation of FoI for populations with seroprevalence approaching 100% since all high FoI values fit the data equally well. This effect is likely reflected by the long tails of some of the posterior distributions presented in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA; we therefore chose to present the posterior mode as the central FoI estimates as we believe this to be a more representative than the mean. The best fitting model matched the data well, with model estimates overlapping the age-stratified seroprevalence data with only a few exceptions (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). The very high levels of seroprevalence measured in young calves in Tunisia and Kingdom of Saudi Arabia (KSA)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e were underestimated, and the model struggled to reproduce the data collected in one study in KSA where seroprevalence was very high in calves and dropped considerably in adults\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, which was not seen in any other study.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePopulation specific estimates of the transmissibility of MERS-CoV in camels.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eDataset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eSeroprevalence (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eTest*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eFoI, l\u003c/p\u003e\n \u003cp\u003eModel 4:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eR\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003erelative infectiousness of reinfections\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" align=\"left\"\u003e\n \u003cp\u003eAfrica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEgypt\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2yrs 37% (n\u0026thinsp;=\u0026thinsp;595)\u003c/p\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;2yrs 82% (n\u0026thinsp;=\u0026thinsp;1946)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5 (0.4, 0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2 (3.7, 5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9 (1.8, 2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEgypt\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2yrs 16% (n\u0026thinsp;=\u0026thinsp;447)\u003c/p\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;2yrs 84% (n\u0026thinsp;=\u0026thinsp;1586)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5 (0.4, 0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0 (3.5, 4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9 (1.8, 2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthiopia\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1- \u0026le;2yrs 93% (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e\n \u003cp\u003e2-13yrs 97% (n\u0026thinsp;=\u0026thinsp;157)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.7 (1.6, 9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.9 (9.6, 44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0 (2.6, 4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;6m 39% (n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e\n \u003cp\u003e6m-2yrs 21% (n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;2yrs 61% (n\u0026thinsp;=\u0026thinsp;194)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2 (0.2, 0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.6 (2.1, 3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7 (1.4, 1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-4yrs 73% (n\u0026thinsp;=\u0026thinsp;285)\u003c/p\u003e\n \u003cp\u003e4-6yrs 98% (n\u0026thinsp;=\u0026thinsp;116)\u003c/p\u003e\n \u003cp\u003e6yrs 98% (n\u0026thinsp;=\u0026thinsp;476)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0 (0.8, 2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.8 (5.5, 13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3 (2.2, 2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;4yrs 36% (n\u0026thinsp;=\u0026thinsp;319)\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;4\u0026thinsp;\u0026lt;\u0026thinsp;7yrs 59% (n\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;7yrs 82% (n\u0026thinsp;=\u0026thinsp;760)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3 (0.2, 0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9 (2.5, 3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7 (1.6, 1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSenegal\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2yrs 29% (n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;2yrs 69% (n\u0026thinsp;=\u0026thinsp;181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3 (0.2, 0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9 (2.3, 4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7 (1.5, 2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTunisia\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2yrs 100% (n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;2yrs 87% (n\u0026thinsp;=\u0026thinsp;754)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8 (0.6, 2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.8 (4.6, 12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2 (2.0, 2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTunisia\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2yrs 30% (n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;2yrs 54% (n\u0026thinsp;=\u0026thinsp;158)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2 (0.1, 0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2 (1.8, 3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5 (1.3, 1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUganda\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2yrs 52% (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;2yrs 66% (n\u0026thinsp;=\u0026thinsp;350)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3 (0.2, 0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.2 (2.6, 4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8 (1.7, 2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" align=\"left\"\u003e\n \u003cp\u003eMiddle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIraq\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2yrs 33% (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;2yrs 57% (n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2 (0.1, 7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.6 (1.7, 35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7 (1.3, 4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIraq\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2yrs 89% (n\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e\n \u003cp\u003e2-4yrs 81% (n\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;4yrs 86% (n\u0026thinsp;=\u0026thinsp;78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8 (0.9, 9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.6 (6.4, 43.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.7 (2.3, 4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJordan\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2yrs 50% (n\u0026thinsp;=\u0026thinsp;82)\u003c/p\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;2yrs 92% (n\u0026thinsp;=\u0026thinsp;222)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0 (0.7, 2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9 (5.0, 13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3 (2.1, 2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJordan\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;2yrs 74%\u003csup\u003ea\u003c/sup\u003e (n\u0026thinsp;=\u0026thinsp;31\u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;2yrs 100%\u003csup\u003ea\u003c/sup\u003e (n\u0026thinsp;=\u0026thinsp;14\u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.6 (1.2, 9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.8 (7.8, 43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0 (2.4, 4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKSA\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e 1992\u0026ndash;2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;2yrs 55% (n\u0026thinsp;=\u0026thinsp;104)\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;2yrs 95% (n\u0026thinsp;=\u0026thinsp;98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8 (1.1, 7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.9 (7.2, 36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.7 (2.4, 4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKSA\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e 2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;2yrs 73% (n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;2yrs 93% (n\u0026thinsp;=\u0026thinsp;187)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2 (0.7, 2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5 (5.3, 15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4 (2.1, 3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKSA\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-2yrs 93% (n\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e\n \u003cp\u003e3-5yrs 78% (n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3 (1.2, 9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.2 (7.5, 43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9 (2.4, 4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKSA\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1\u0026nbsp;year 72% (n\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e\n \u003cp\u003e1-3yrs 95% (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e\n \u003cp\u003e4-5yrs 97% (n\u0026thinsp;=\u0026thinsp;76) \u0026gt;5yrs 92% (n\u0026thinsp;=\u0026thinsp;63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eppNT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0 (1.7, 9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.6 (10.4, 41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.1 (2.6, 4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKSA\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2yrs 82% (n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;2yrs 82% (n\u0026thinsp;=\u0026thinsp;211)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6 (0.4, 2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.5 (3.4, 12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0 (1.8, 2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUAE\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;1\u0026nbsp;year 85% (n\u0026thinsp;=\u0026thinsp;108)\u003c/p\u003e\n \u003cp\u003e2-4yrs 97% (n\u0026thinsp;=\u0026thinsp;340)\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;4yrs 96% (n\u0026thinsp;=\u0026thinsp;310)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1 (3.5, 9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.7 (18.5, 44.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2 (3.2, 4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eSouth Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBangladesh\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2yrs 9% (n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;2yrs 36% (n\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eppNT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1 (0.0, 0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7 (1.3, 3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3 (1.1, 1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePakistan\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;2yrs 29% (n\u0026thinsp;=\u0026thinsp;89)\u003c/p\u003e\n \u003cp\u003e2.1-5yrs 30% (n\u0026thinsp;=\u0026thinsp;208)\u003c/p\u003e\n \u003cp\u003e5.1-10yrs 51% (n\u0026thinsp;=\u0026thinsp;180)\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;10yrs 49% (n\u0026thinsp;=\u0026thinsp;88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1 (0.1, 0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9 (1.7, 2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4 (1.3, 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePakistan\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;3yrs 58% (n\u0026thinsp;=\u0026thinsp;177)\u003c/p\u003e\n \u003cp\u003e3.1-10yrs 79% (n\u0026thinsp;=\u0026thinsp;712)\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;10yrs 81% (n\u0026thinsp;=\u0026thinsp;161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eELISA then MN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5 (0.4, 0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.9 (3.3, 4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9 (1.8, 2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRate of waning mAbs, w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.9 (3.2, 9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRate of waning Abs, s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06 (0.03, 0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverdispersion, k\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5 (2.0, 3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e*MN\u0026thinsp;=\u0026thinsp;micro neutralisation test, ppNT\u0026thinsp;=\u0026thinsp;pseudo particle neutralisation test, PM\u0026thinsp;=\u0026thinsp;protein micro-array, ELISA\u0026thinsp;=\u0026thinsp;Enzyme linked immunosorbent Assay.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eBasic reproduction number\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eBasic reproduction number \u003cem\u003e(R\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)\u003c/em\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eR\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003e0\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e estimates can provide a more widely used intuitive measure of transmissibility which are more readily comparable with other diseases. We translated FoI into \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e using a dynamic, age-stratified, stochastic model of MERS-CoV transmission (please see \u003cem\u003eMethods\u003c/em\u003e for a detailed description of model assumptions). Estimates were sensitive to the assumed relative infectiousness of reinfected animals (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). When reinfected animals were assumed to be 1% as infectious as animals experiencing a primary infection (based on viral shedding data from the control arm of the ChAdOx vaccine field study\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e), central \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e estimates ranged from 3 to 34 in the Middle East, compared with 2 to 15 in populations sampled in Africa and 2 to 4 in South Asia. As a sensitivity analysis, when infectiousness was assumed to be only halved in reinfections (based on assuming a logarithmic rather than linear relationship between the measured viral shedding and infectiousness), \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e ranged from 2 to 4 in the Middle East, 1 to 3 in Africa, and 1 to 2 in South Asia. \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e estimates for populations with very high FoI estimates were most sensitive to assumptions about immunity as, in these populations, a higher proportion of infections at endemic equilibrium are reinfections and are therefore affected by relative infectiousness parameters. Neither varying the duration of complete immunity following infection, nor the relative susceptibility of previously infected individuals had a considerable effect on \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e estimates (\u003cem\u003eTable S4\u003c/em\u003e). When using estimates for the longer duration of mAbs (4.2 months) and for the FoI from the second-best fitting model without seroreversion, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e estimates were similar to those using best fitting model with seroreversion, albeit slightly lower (\u003cem\u003eTable S4\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eThe critical community size (CCS)\u003c/p\u003e\n\u003cp\u003eWe used the transmission model to estimate the population size above which the extinction of MERS-CoV transmission by chance becomes unlikely - the critical community size (CCS)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. We considered three different transmissibility levels spanning our \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e estimates across different settings: low (South Asia and parts of Africa), moderate (Kenya and parts of Middle East), and high (Ethiopia and parts of Middle East) (\u003cem\u003eFigure S4\u003c/em\u003e). The CCS varied between approximately 10,000\u0026ndash;70,000 camels depending on the transmissibility, seasonality of calving and underlying herd structure assumed in the transmission model (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The CCS decreased as transmission intensity increased, except for when births were highly seasonal, and the population was modelled as homogeneous. In this case, high transmissibility resulted in a larger CCS than low or moderate transmissibility. This likely represents a complex interaction between seasonality, transmissibility, and accumulation of susceptible individuals. Transmission could be sustained in smaller populations when births were less seasonally forced, and when the population was assumed to be homogenous as opposed to being structured into weakly connected patches intended to represent large herds or communities. Under our alternative assumption that viral shedding is proportional to the log of infectiousness meaning past infection reduces infectiousness by only 50%, the CCS was smaller, with only 1,000\u0026ndash;30,000 camels needed to sustain transmission across depending on the transmission setting (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 2.\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;The estimated critical community size of MERS-CoV in camels under two alternative values for the relative infectiousness of reinfected animals (r\u003csub\u003einf\u003c/sub\u003e), in different transmission settings (R\u003csub\u003e0\u003c/sub\u003e). Estimates are shown for models assuming a homogeneous population of perfectly mixed animals and a structured population in which animals have more contact with those in their \u0026ldquo;patch\u0026rdquo; and weaker contact with those in surrounding patches. Estimates are shown assuming births follow seasonal patterns as strong as those observed in KSA (d = 1) and alternatively with a weaker seasonality (d = 0.5).\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ePeriodicity of transmission\u003c/p\u003e\n\u003cp\u003eWhen births are assumed to follow seasonal patterns representative of those observed in KSA (see \u003cem\u003eMethods\u003c/em\u003e), the number of infections over time has an annual periodicity in large populations, with peaks of a similar size occurring each year (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.A). In small populations, or when transmissibility is low, reflecting estimates for camel populations in South Asia and parts of Africa, biennial, triennial, and quadrennial periodicities - with patterns in the magnitude of annual peaks in infections repeating over 2-, 3- or 4-year cycles \u0026ndash; are detected based on autocorrelation coefficients in a proportion of stochastic iterations (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.B). No seasonality in infections is observed when births are non-seasonal.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe impact of vaccination\u003c/p\u003e\n\u003ch3\u003eOptimal target age\u003c/h3\u003e\n\u003cp\u003eWe extended the transmission model to simulate the impact of age-targeted vaccination under multiple efficacy scenarios based on RNA shedding data from field studies and remaining uncertainties (\u003cem\u003eMethods\u003c/em\u003e). The optimal target age for routine vaccination was assessed in a large population of camels so that overarching trends were not obscured by stochasticity. In our conservative scenario (scenario 1), in which vaccination reduces infectiousness of subsequent infections in all vaccinated animals but had no effect on susceptibility, vaccination led to the greatest reduction in infection incidence when calves were targeted in the first few months of life (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). In the absence of vaccination, most animals were first infected when they were \u0026lt;\u0026thinsp;1-yr old across all modelled transmission settings. In targeting younger calves, vaccination precedes first infection in a greater number of individuals, reducing their subsequent infectiousness. The reduction in incidence afforded by targeting younger animals was larger in higher transmission settings where first infections occurred earlier, with reductions in incidence diminishing quicker as the target age class was increased compared to in lower transmission settings. When the duration of vaccine induced effects was assumed to be relatively short and transmission intensity was moderate or high, vaccinating very young calves shifted the average time to first infection into older age groups, leading to a small increase in the annual incidence in adult animals of up to 10 per 1000 animals under our core model assumptions (\u003cem\u003eFigure S6\u003c/em\u003e). Vaccinating at 6 months allowed large reductions in overall incidence of infection without seeing considerable shifting of first infections into adult animals. Note that adult incidence is considered here given it may be a proxy for zoonotic spillover risk to humans.\u003c/p\u003e\n\u003cp\u003eUnder our optimistic scenario (scenario 2) in which we assumed vaccination reduced both infectiousness and susceptibility in all vaccinated animals, we saw the same pattern as in scenario 1, with greatest impact achieved by vaccinating in the first few months of life, and no notable increase in adult incidence when targeting 6-month-olds. Vaccinating older age groups after most first infections had occurred had almost no effect on incidence in scenario 1, whereas a slight reduction was still achieved by reducing the susceptibility of the older animals to reinfection in scenario 2 (\u003cem\u003eFigure S7\u003c/em\u003e). In our third scenario, in which vaccination was only effective as a booster for previously infected animals with no impact in na\u0026iuml;ve animals, the optimal age for vaccination was early adulthood but even then, the reduction in incidence was minimal at \u0026lt;\u0026thinsp;8% across all transmission intensities (\u003cem\u003eFigure S7).\u003c/em\u003e The optimal target age for vaccination was robust to our different assumptions about the relationship between viral load and infectiousness.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eThe impact of vaccination on transmission\u003c/h3\u003e\n\u003cp\u003eTo explore the characteristics of the MERS-CoV vaccine and the vaccine coverage that would be necessary to achieve considerable reductions in infection incidence among camels, we simulated the impact of vaccination of 6-month-old calves in two modelled populations: one of 2\u0026nbsp;million camels comparable in size to that of KSA; and one of 75,000 camels comparable to that of a small camel-keeping Kenyan county.\u003c/p\u003e\n\u003cp\u003eIn a population of 2 million camels divided into large homogenous patches, assuming the vaccine reduces infectiousness but not susceptibility, the vaccination coverage required to half the total incidence over the 10 years following introduction was between 50\u0026ndash;90% in 6-month-olds, depending on the duration of vaccine induced effects and the transmission intensity (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). When vaccine induced effects were long lasting, 50% coverage was required to half incidence in low transmission intensity settings, rising to approximately 80% coverage needed in high transmission intensity settings (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). When effects lasted 3 years, coverage of 70% was needed in low transmission settings rising to 90% when transmission intensity was high, and when vaccine induced effects only lasted one year, incidence could not be halved under any modelled setting. Alternatively, in a population of 75,000 camels, stochastic effects amplified the impact of vaccination: a coverage of \u0026lt;\u0026thinsp;=\u0026thinsp;50% in 6-month-olds could half total incidence in the 10 years following vaccine in low transmission intensity settings, even if effects only lasted 1 year. In a moderate transmission intensity setting, between 50\u0026ndash;70% coverage was needed, and in high transmission intensity settings 70\u0026ndash;90% coverage, depending on duration of vaccine induced effects. Across all transmission intensities, assuming the vaccine reduced susceptibility of vaccinated animals to 50% or 75% (efficacy scenario 2) only afforded a very small (~\u0026thinsp;1% on average) additional reduction in incidence compared to when assuming the vaccine reduced infectiousness alone (\u003cem\u003eFigure S8\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eIn the population of 2 million, when \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e was low, coverage was high, and the effects of the vaccine were long-lasting, vaccination was capable of interrupting transmission and led to stochastic fadeout. In these cases, the difference in incidence between stochastic runs was often large (the 2.5% and 97.5% quantiles are represented by transparent ribbons in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). In low and moderate intensity settings, vaccination was able to interrupt transmission when coverage in 6-month-olds was very high and the vaccine induced effects lasted at least 3 years (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In high transmission intensity settings transmission was only interrupted when coverage was 100% and vaccine induced effects lasted 10 years. In the smaller population of 75,000 divided into homogenous patches of 3,000, stochastic fadeout occurred at lower coverages and across a wider range of scenarios. Vaccination was capable of reliably interrupting transmission when coverage ranged from 40\u0026ndash;80% depending on transmission intensity and duration of vaccine induced effects.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe coverage necessary to interrupt transmission at the population level in two modelled populations\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"Underline\"\u003eVaccine coverage (%) needed to interrupt transmission in a population of\u003c/span\u003e:\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/\u0026rho;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e75,000\u003c/strong\u003e \u003cem\u003esplit into patches of 3,000\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2,000,000\u003c/strong\u003e \u003cem\u003esplit into patches of 80,000\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e14.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUnderstanding the transmission dynamics of MERS-CoV in camels is vital to evaluating the potential public health impacts of animal vaccination but has been hindered by the scarcity of data describing what is largely an asymptomatic infection in this species. By using age-stratified seroprevalence and viral load data extracted from published studies, we estimated the transmissibility of MERS-CoV in camels and developed a dynamic model of transmission, allowing for the first evaluations of the potential impact of camel vaccination under different efficacy scenarios. Whilst considerable uncertainty around immunity and aspects of vaccine efficacy remains, we have gained several insights into the transmission dynamics and controllability of MERS-CoV in camels.\u003c/p\u003e \u003cp\u003eThe transmissibility of MERS-CoV was generally estimated to be higher in camel populations in the Middle East compared to those sampled in South Asia and Africa. All viruses sampled from camels in Africa have been classified into Clade C based on their genetic similarities. Strikingly, despite the large number of live camels imported from Africa, all viruses isolated from camels and humans in the Arabian Peninsula have belonged to genetically distinct clade A and B viruses \u0026ndash; even those isolated from newly imported animals\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Our estimates of higher MERS-CoV transmissibility in camels in the Middle East align with the results of a recent study that found clade C to have a reproductive disadvantage compared with clade A and B in human lung tissue\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, suggesting that the clade C viruses prevalent in Africa may be intrinsically less transmissible to humans, and perhaps between camels as well. However, by underpinning interactions between susceptible and infectious animals, variation in global camel husbandry practices could also potentially explain differences in transmissibility. The camel population is highly heterogeneous in terms of husbandry practice even at the local scale\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In the Arabian Peninsula, camel farming has become increasingly intensive and urban in the past 60 years, whilst remaining largely extensive pastoralist techniques elsewhere\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Further investigation of what is driving perceived differences between transmission intensities will be important for devising context-specific vaccination or other control strategies.\u003c/p\u003e \u003cp\u003eIt is important to highlight several limitations that could also have affected our transmissibility estimates. Firstly, the camel populations reflected in the FoI and \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e estimates are based on age stratified seroprevalence surveys and are likely to be affected by several biases including bias towards countries with sufficient resources to detect human cases and to study MERS-CoV in their camel populations. It was not possible to estimate the transmissibility of MERS-CoV in Somalia and Sudan \u0026ndash; the most camel dense areas in the world \u0026ndash; due to a lack of age stratified seroprevalence data for these populations. Secondly, surveys used different tests to determine seropositivity, likely with different sensitivity and specificity. Since the FoI is estimated using relative differences in seroprevalence within a single study where a single test type was used, our estimates should not be greatly affected except when seroprevalence is very high as it may approach the limit of sensitivity. To test the influence of differences in test sensitivity and specificity across test types, models were re-fit to the data whilst assuming reduced specificity of ELISAs and reduced sensitivity of neutralisation tests. Our ranking of transmissibility, with generally higher estimates in the Middle East and lower in Africa and South Asia, was robust to this change. Finally, the rate of seroreversion could not be reliably identified due to uncertainty around test sensitivity and challenges distinguishing long-lasting antibodies from repeated boosting of antibodies following recurring infection in the catalytic model framework. Documented reinfection of seropositive animals shows that antibodies are not a proxy for complete immunity to MERS-CoV. With this in mind, the rate of seroreversion was not used to inform parameterisation of waning immunity in the transmission model, instead several alternative values were used, meaning this limitation did not impinge the vaccine impact modelling.\u003c/p\u003e \u003cp\u003eOur estimates of the CCS were larger than most reported herd sizes which tend to be well under 1,000 animals\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, emphasising the importance of focusing interventions on reducing inter-herd infections for interrupting transmission. The dependence of the CCS on transmissibility, together with the difference in \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e across populations, suggests that MERS-CoV may be able to persist in a population 2\u0026ndash;20 times smaller in high transmission settings found in the Arabian Peninsula, compared to lower transmission settings. The results of our simulations suggest that seasonality of births can be expected to drive annual, seasonal peaks in infection in large populations when transmission intensity is comparable to the high \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e estimates for MERS-CoV in populations in parts of the Middle East, and Ethiopia. Whilst infections tended to peak outside of the calving season, the lag between the simulated peak in births and annual peak in infection depended on \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e. If MERS-CoV infections peak annually in some settings as these simulations suggest, there may be seasons in which risk of zoonotic transmission is elevated. Better understanding of when peaks occur would provide opportunities to mitigate risk and avert human cases. Although we considered seasonal calving, other factors such as annual migrations and events that affect camel mixing could also affect the transmission dynamics of MERS-CoV in the zoonotic reservoir. Ultimately, it is necessary to undertake long-term surveillance over several years to better ascertain the seasonality of MERS-CoV in camels.\u003c/p\u003e \u003cp\u003eOur vaccination simulations indicated that if a MERS-CoV vaccine is able to reduce infectiousness in na\u0026iuml;ve and previously infected camels, large reductions in incidence are possible, provided that a high proportion of calves are vaccinated. Little impact was seen if the vaccine was only effective in previously infected animals. Although the ChAdOx1 MERS vaccine was measured to have poor efficacy in na\u0026iuml;ve animals in an initial field trial, potentially due to the animals\u0026rsquo; age\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, the MVA vaccine has been shown to reduce shedding in na\u0026iuml;ve animals\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Assuming independence of efficacy on age, we saw that vaccinating calves in their first few months of life maximises reductions in overall incidence among camels. Our observation that under some scenarios vaccination of very young calves led to more infections in adult animals, highlights the importance of understanding the age dependency of human-camel contact patterns across different populations. Vaccination strategies should be evaluated not only on their likely impact on transmission amongst camels, but also on the age distribution of infections in light of such contact patterns.\u003c/p\u003e \u003cp\u003eWhen simulated vaccine coverage was high, vaccination led to large reductions in infection incidence even when the vaccine was only assumed to reduce infectiousness rather than susceptibility. When infectiousness was assumed to be proportional to viral RNA shedding, the coverage needed to interrupt transmission in a population of 2\u0026nbsp;million was \u0026gt;\u0026thinsp;70% across all scenarios, reaching 90\u0026ndash;100% in moderate-high transmission intensity settings. This suggests that, in a large population with extremely high levels of camel mixing it would be difficult to entirely interrupt transmission in some Middle Eastern settings by using vaccination of calves alone, but that incidence could still be greatly reduced. Our estimates are limited by the absence of data on true population structures and movement. Instead, a rudimentary grid of connected sub-populations was used to approximate the structuring of the population into herds or patches. In smaller or more fragmented populations with less mixing interruption could likely be achieved with a lower coverage. Tailoring the model to specific populations using camel herd size, contact and movement data would be necessary to better evaluate the likely impact of vaccination in populations of most interest to decision makers. Two additional data gaps limited our assessment of vaccine impact. Firstly, the undefined relationship between viral RNA shedding data and infectiousness. If rather than being proportional to viral shedding, the relationship is closer to being logarithmic, meaning vaccination leads to a much smaller decrease in infectiousness, then our results suggest that large reductions in incidence would be much more difficult to achieve and require even higher vaccine coverage. Secondly, the effect of vaccination on susceptibility and vaccine effectiveness in na\u0026iuml;ve animals were not clear from current field trials so we included a range of scenarios as sensitivity analyses. As these aspects of MERS-CoV transmission and vaccination in camels become better characterised, it will be possible to improve mathematical models of MERS-CoV transmission and become increasingly confident that they accurately represent transmission in the zoonotic reservoir.\u003c/p\u003e \u003cp\u003eModels of MERS-CoV transmission have previously focused on human-to-human transmission. However, as recurring camel-to-human transmission drives human cases there was a growing need for a model of transmission in the zoonotic reservoir. The model presented here provides a framework in which to simulate MERS-CoV vaccination strategies in camels which, together with improved data on camel mixing patterns and further empirical studies of vaccine efficacy, could offer an important contribution to inform effective responses to the zoonotic transmission of MERS-CoV. Efforts to better define the relationship between the number of infectious camels and the risk of zoonotic spillover events would allow the expected reduction in infection amongst camels to be translated into the expected number of human cases averted, permitting evaluation of the cost-effectiveness of camel vaccination as an intervention against human cases of MERS-CoV.\u003c/p\u003e "},{"header":"Methods","content":" \u003cp\u003eFurther details of methods are given in the Supplementary Materials.\u003c/p\u003e \u003cp\u003eEstimating the transmissibility of MERS-CoV in camels\u003c/p\u003e \u003cp\u003eWe estimated two different measures of the transmissibility of MERS-CoV in camels: the Force of Infection (FoI, l) defined as the rate at which susceptible animals become infected, and the reproduction number (R\u003csub\u003e0\u003c/sub\u003e) defined as mean number of individuals infected by a single infected individual in an entirely susceptible population.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFoI\u003c/h2\u003e \u003cp\u003eWe fitted catalytic models of seroconversion to age-stratified seroprevalence estimates from across Africa, South Asia and the Middle East collated previously through a systematic review\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Since the catalytic modelling approach assumes seroprevalence estimates are derived from a random cross-sectional sample of individuals, we excluded 3 of the 19 reviewed studies based on their sampling strategies (please see \u003cem\u003eSupplementary Materials\u003c/em\u003e for more details on study inclusion). To make the geographical range of the FoI estimates as comprehensive as possible, we used seroprevalence measures from one additional study published after the systematic review. This allowed us to include camel populations in Senegal and Uganda which were not previously represented in the literature\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The tests used to determine seropositivity varied between studies and included both neutralisation tests (NTs) and non-neutralising Enzyme Linked Immunosorbent Assays (ELISAs) \u003cem\u003e(\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e. NTs are shown to be highly specific to MERS-CoV antibodies with little cross-reactivity with other camel coronaviruses\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. MERS-CoV IgG ELISAs have been measured to be 99% specific when correlated with NTs\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Whilst ELISAs are considered more sensitive than NTs as they can pick up non-neutralising antibodies\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, the seroprevalence measured by studies using NTs often approaches or reaches 100% in adult camels suggesting that \u0026ndash; assuming they are indeed highly specific \u0026ndash; they must also be highly sensitive. Therefore, we assumed a high sensitivity (98% for both test types) and specificity (99.5% for NTs and 98.5% for non-NTs) in our core results. We then conducted a sensitivity analysis assuming NTs to have a lower sensitivity of 85%.\u003c/p\u003e \u003cp\u003eWe compared the fit of four models of seroconversion. In model 1 we assumed that all animals are born seronegative and become seropositive at a constant rate l, as originally conceptualised by Muench and now regularly applied to epidemiological data\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Since MERS-CoV reinfection has been documented in camels\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, in model 2 we extended model 1 to allow for seroreversion - with protective antibodies waning at rate s. In model 3 we extended model 1 to allow a proportion of calves to be born seropositive due to protective mAbs which wane at rate w, as evidence suggests that calves born to seropositive mothers are shown to acquire MERS-CoV specific mAbs through colostrum\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Finally, our fourth model allowed for both mAbs and seroreversion. Please see the \u003cem\u003eSupplementary Materials\u003c/em\u003e for equations describing the solutions used for each model. We fit the models within a Bayesian framework using Hamiltonian Monte Carlo (HMC) sampling algorithm implemented in the R software package rstan\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Whilst we estimated the FoI per study to account for potential true differences between the FoI across husbandry systems, we assumed antibody waning rates to be constant, estimating them globally across all the datasets. We assumed that the seroprevalence data was beta-binomially distributed and re-parameterised the beta-binomial distribution in terms of the mean probability of being seropositive and the overdispersion parameter k where k\u0026thinsp;\u0026gt;\u0026thinsp;0 and a k approaching zero would indicate negligible overdispersion. A detailed reparameterization available in the \u003cem\u003eSupplementary Material\u003c/em\u003e. In order to evaluate which of the models was best supported by the data, we compared their fit using the Deviance Information Criterion\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe reproduction number (R)\u003c/h3\u003e\n\u003cp\u003eWe estimated \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e of MERS-CoV in each study population by calibrating a dynamic model of MERS-CoV transmission (see next section) to the modal FoI, by varying the transmission intensity parameter, b, under different potential immunity scenarios. \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e was approximated as the product of b and the infectious period, g. The one-to-one relationship between b and the FoI meant that the credible intervals (CrIs) around the FoI could be used to propagate the uncertainty into the \u003cem\u003eR0\u003c/em\u003e estimates.\u003c/p\u003e \u003cp\u003eDevelopment of a dynamic model of MERS-CoV transmission in camels\u003c/p\u003e\n\u003ch3\u003eInfection\u003c/h3\u003e\n\u003cp\u003eBased on what we know about camel demography from the literature, and our estimates of transmissibility and maternal antibody waning, we developed a stochastic, age-structured model of MERS-CoV transmission in camels. The model structure is represented schematically in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, with a single age class shown for clarity. All symbols used are defined in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e alongside the parameter values and their sources. Camels are born either entirely susceptible to MERS-CoV infection (state \u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e) or with complete protection by mAbs (state \u003cem\u003eM\u003c/em\u003e) which wanes at a rate \u003cem\u003eω\u003c/em\u003e with calves becoming susceptible after an exponentially distributed period with a mean of ~\u0026thinsp;2 months as estimated from the age-stratified seroprevalence data. The proportion of calves born in state \u003cem\u003eM\u003c/em\u003e is dictated by the proportion of animals of reproductive age (\u0026gt;\u0026thinsp;4 years) which have been previously infected. Animals in \u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e become infected and transition to state \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e with the FoI, \u003cem\u003eλ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e, defined as the product of the effective contact rate, \u003cem\u003eβ\u003c/em\u003e, and the proportion of individuals in the population which are infectious:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{\\lambda\\:}_{1}=\\beta\\:\\frac{{I}_{1}+r{I}_{2}}{N}\\: \\:1\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e is the number of reinfected individuals, \u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1 and represents the relative infectiousness of reinfections compared to first infections, and \u003cem\u003eβ\u003c/em\u003e is varied to calibrate \u003cem\u003eλ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e to our FoI estimates from age-stratified seroprevalence data. No data is available on the potential latent period following MERS-CoV infection in camels. Infected animals are assumed to be instantaneously infectious. The period spent in state \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e is exponentially distributed around a mean of 14 days in agreement with the duration of shedding reported in longitudinal studies\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTransmission model parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDescription\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eValues\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSource\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInitial population size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVaried (50\u0026thinsp;\u0026minus;\u0026thinsp;10,000,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026#120572;̅\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean birth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVaried annually around a mean of 0.000565 camel\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e based on initial population size.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEstimates of 45.2% annual fecundity in KSA\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e taken together with assumptions that 90% of the population are female due to high male removal rate\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and that 50% of the female population are of reproductive age\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffective contact rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1-1.0 camel\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCalibrated to our FoI estimates from age-stratified seroprevalence data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eγ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRate of recovery from infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/14 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eδ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrength of seasonality of births\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0, 0.5 also considered)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e see \u0026ldquo;Births\u0026rdquo; in \u003cem\u003eMethods\u003c/em\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eσ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRate of waning immunity following infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/30 days, 1/90 days (scenario with no complete immunity also considered)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eλ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRate at which susceptible animals become infected, equal to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\frac{{I}_{1}+r{I}_{2}}{N}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1-3.0 calibrated by varying \u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOur estimates from age-stratified seroprevalence data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eω\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRate of waning of mAbs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0136 day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOur estimates from age-stratified seroprevalence data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaily mortality rate of camels\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;2yrs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0011 camel\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWithin the ranges described by \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e but exact value set to balance mean birth-rate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaily mortality rate of camels aged\u0026thinsp;\u0026gt;\u0026thinsp;2yrs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00036 camel\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWithin the ranges described in \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e but exact value set to balance mean birth-rate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eφ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSusceptibility to reinfection relative to first infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75 (0\u0026ndash;1 considered)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfectiousness of reinfections relative to first infections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01, 0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e see \u0026ldquo;Immunity\u0026rdquo; in \u003cem\u003eMethods\u003c/em\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImmunity\u003c/h2\u003e \u003cp\u003eWhilst our inference from age-stratified seroprevalence suggests that under catalytic model assumptions antibodies may be long-lasting following infection, documented reinfection of seropositive animals and rapid reinfection in high transmission intensity environments indicates that MERS-CoV seropositivity is not a good proxy for protective immunity in camels\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. We therefore explored multiple reinfection scenarios (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Following a short period of complete immunity in state \u003cem\u003eR\u003c/em\u003e, individuals become susceptible to reinfection in state \u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e. Most animals found to be shedding MERS-CoV in field surveys are calves and na\u0026iuml;ve animals, suggesting there is some long-term protection offered by past infection\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. To reflect this, the degree of susceptibility in state \u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e is modelled to be less than that experienced by individuals in state \u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e, meaning individuals in state \u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e experience a reduced FoI, \u003cem\u003eλ\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e. Reinfected individuals in \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e. are modelled to be less infectious than individuals in \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e. This is based on measures of viral load collected in the control arm of the ChadOx1 MERS vaccine field study in camels\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. We digitally extracted the daily mean viral load for seronegative calves (which we assumed to be infected for the first time during the study) and seropositive calves (which we assumed to be reinfected during the study) in the unvaccinated control group in from Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA of the online publication using PlotDigitizer version 2.2\u003csup\u003e48\u003c/sup\u003e. We then calculated the difference between the area under the viral load curve for each of the two groups. Reinfected animals were approximately 1% as infectious as first-time infected animals when assuming a linear relationship between viral load and infectiousness. The relationship between viral load and infectiousness is not well characterised. A trial of the MVA-based vaccine candidate in camels measured a similar decrease in a measure of infectious viral particles and a measure of viral RNA shedding, following vaccination of four calves\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, the study was not designed to have the power to reliably define the relationship between infectious virus particles and infectiousness. Therefore, whilst our core results assume a linear relationship, with reinfected individuals 1% as infectious as first-time infections, we include a sensitivity analysis assuming that the relationship between viral load and infectiousness is logarithmic, with a relative infectiousness of 50% for reinfected animals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAge structure\u003c/h2\u003e \u003cp\u003eInclusion of age structure is vital given the strong dependence of infection status and seroprevalence on age, as well as for simulating age targeted interventions. Fine age structure is especially important up until the age of four years to enable accurate representation of age within the window where first infections are happening and accurate, age-targeted intervention modelling. For this reason, the model is stratified into month-wide classes, with camels moving to the next age-strata every 30 days in a 360-day year. From the 48th month wide class, camels enter a class aged\u0026thinsp;\u0026gt;\u0026thinsp;4 years where they remain until death.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBirths\u003c/h2\u003e \u003cp\u003eCamel calving is reported to be strongly seasonal\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan additionalcitationids=\"CR50 CR51 CR52\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Studies in KSA report most calves being born between October and March, with one study quantifying this at 83% during the high season\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The calving season is very similar in Egypt where it is reported between October and April\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e and in Nigeria where surveyed pastoralists identified the calving peak to occur in the early dry season between October to December\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. To capture this seasonality, the number of births per day is drawn from a Poisson distribution with a mean of \u003cem\u003eαΝ\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e where \u003cem\u003eα\u003c/em\u003e varies annually as a function of cosine (Equations 2\u0026amp;3) and \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e is the initial population size. The strength of seasonality can be weakened by setting \u003cem\u003eδ\u0026thinsp;\u0026lt;\u0026thinsp;1\u003c/em\u003e during sensitivity analyses. However, when \u003cem\u003eδ\u0026thinsp;=\u003c/em\u003e\u0026thinsp;1, 82% of births fall between October and March which is in line with the 83% reported for camel births in Qassim, KSA\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}births\\:\\sim\\:Pois\\left(\\alpha\\:\\left(t\\right)*{N}_{0}\\right) 2\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}\\alpha\\:\\left(t\\right)=\\stackrel{-}{\\alpha\\:\\left\\{1+\\text{cos}\\left(\\frac{2\\pi\\:t}{360}\\right)\\right\\}}\\: 3\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDeaths\u003c/h2\u003e \u003cp\u003eSince MERS-CoV causes very mild disease in camels, infection is modelled to have no bearing on mortality. Camels die off from each disease state compartment at the same age dependent average rate \u0026micro;i, with the mean number of deaths per day being equal to the size of the compartment multiplied by \u003cem\u003e1 \u0026ndash; e -\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e. The model assumes a higher probability of calf death in the first two years of life than in adulthood, as reported in KSA\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. The modelled mortality rates are calibrated to the birth rate to give a stable population size and are equivalent to ~\u0026thinsp;40% mortality in the first two years of life and ~\u0026thinsp;12% afterwards, similar to overall mortality estimates for populations in KSA which are described in the literature as 10\u0026ndash;26%, depending on herd type\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStructure\u003c/h2\u003e \u003cp\u003eFor large populations, it becomes unrealistic to assume populations are well mixed. For example, in the population of ~\u0026thinsp;10,000 camels in Laikipia county, Kenya\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, an individual camel is far more likely to have contact with individuals in its own herd or grazing area than with animals in other areas of the county. The movements and interactions between herds of camels are not well documented. To explore the effect population structure has on dynamics, we developed a rudimentary structured population model where sub-populations or patches are arranged over a grid (\u003cem\u003eFigure S5\u003c/em\u003e). Individuals are most likely to be in contact with other individuals in the same patch, less likely to meet individuals in neighbouring patches, and do not meet individuals in distant patches. Until better data on population structure allows a more accurate representation of networks and movements of camels within a region, the grid serves as a na\u0026iuml;ve representation of this reality.\u003c/p\u003e \u003cp\u003eWe coded the model in R\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e version 3.5.3, using the package odin\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e and ran stochastic iterations using odin.dust\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEstimating the Critical Community Size (CCS)\u003c/p\u003e \u003cp\u003eTo evaluate the CCS of MERS-CoV in camel populations, we estimated the size of the population required for transmission to be sustained for at least 25 years in a closed population with no external sources of infection. The CCS was defined as the population size at which transmission was sustained in at least 50% of stochastic model runs. We ran the model using population sizes ranging from 500-1000000 and estimated the precise population at which 50% persistence was achieved using linear interpolation.\u003c/p\u003e \u003cp\u003eEvaluating the periodicity of infections\u003c/p\u003e \u003cp\u003eTo determine the average time between peaks in infections we estimated the autocorrelation between each simulated time series of infections and lagged versions of itself using Pearson\u0026rsquo;s correlation test implemented through the acf function in the R \u0026ldquo;stats\u0026rdquo; package. The lag that maximised the autocorrelation coefficient was used to estimate the periodicity, for example if the lag that maximised the autocorrelation coefficient was between 350\u0026ndash;370 periodicity was classified as annual. Very short lags of \u0026lt;\u0026thinsp;100 days and any acf below the significance level using 95% confidence interval (CI) were excluded.\u003c/p\u003e \u003cp\u003eEstimating vaccine impact\u003c/p\u003e \u003cp\u003eWe extended the transmission model to simulate vaccination by duplicating the set of disease states to create a parallel set of vaccinated states. Although two vaccine candidates have been shown to reduce viral shedding in camels, uncertainty remains around their ability to reduce susceptibility and around the effectiveness of the ChAdOx1 MERS vaccine in na\u0026iuml;ve animals. Due to these uncertainties, three main scenarios are modelled (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In our core scenario 1, the vaccine reduces infectiousness but \u003cem\u003enot\u003c/em\u003e susceptibility to infection for all vaccinated animals. This scenario reflects the finding that all previously na\u0026iuml;ve vaccinated animals became infected when challenged. Challenge doses administered intranasally or by confinement with multiple infectious animals could be much higher than the average natural exposure, and 1/5 of the previously infected ChAdox1 vaccinated animal did not become infected despite challenge so we also explored an alternative scenario 2 in which the vaccine reduces both infectiousness and susceptibility for all vaccinated animals. Finally, we explored a third scenario in which the vaccine reduces both infectiousness and susceptibility but only in animals that have been previously infected. Although the MVA study measured a large reduction in infectiousness of previously seronegative vaccinated animals, the ChadOx1 vaccine was only measured to reduce shedding in previously infected vaccinated animals. Authors suggest the low efficacy in this group could be due to the naive animals being very young, but their age was comparable with those used in the MVA study. Parameters used in vaccination simulation are presented in \u003cem\u003eTable S5\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDue to the scope of the efficacy studies, it is not possible to estimate the rate of waning of vaccine-induced effects, 1/ρ. Instead, for each main scenario, three options are explored with effects lasting one, three and ten years. The relative infectiousness of vaccinated infected animals and of vaccinated reinfected animals compared to unvaccinated na\u0026iuml;ve animals was parameterised using viral RNA shedding data\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, assuming that infectiousness is either proportional to viral RNA shedding or to the log of viral RNA shedding. Vaccination is implemented in an age dependent manner and occurs immediately at the point at which camels reach the age being targeted for vaccination. To evaluate the ideal age for vaccination under the model assumptions, the target age group was varied from one month old to four years old. The vaccine efficacy was not modelled to vary with age. In scenarios 1 and 2 vaccination was assumed to reduce the relative infectiousness of first-time infected animals (\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003ev\u003c/em\u003e\u003c/sub\u003e) by the same amount as natural infection reduces viral shedding in reinfected individuals. The relative infectiousness of reinfected vaccinated individuals (\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003einf_v\u003c/em\u003e\u003c/sub\u003e) was estimated as 0.15% when a linear relationship between infectiousness and viral RNA shedding was assumed, and 33% when infectiousness was assumed to be proportional to the log of viral RNA shedding\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. See Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e for the complete set of parameters used to model the effect of vaccination. Vaccine impact was measured as difference in incidence following annual vaccination over a ten-year period and potential to disrupt patch or population-level transmission.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters used to simulate vaccination under different efficacy scenarios\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDescription\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eValues\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSource\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003ev\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelative infectiousness of vaccinated animals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1% (50% as sensitivity analysis assuming infectiousness is proportional to the logarithm of viral load).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003einf_v\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelative infectiousness of vaccinated previously infected animals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15% (33% as sensitivity analysis assuming infectiousness is proportional to the logarithm of viral load).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eφ\u003c/em\u003e\u003csub\u003e\u003cem\u003ev\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelative susceptibility of vaccinated animals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100% (or 75% in scenario 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA. Larger reduction in susceptibility unlikely given all vaccinated previously seronegative animals were infected in \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eφ\u003c/em\u003e\u003csub\u003e\u003cem\u003einf_v\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelative susceptibility of vaccinated previously infected animals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75% (or 75% in scenario 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA. Indication of reduced susceptibility\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e but unable to reliably quantify in small population\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e1/ρ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe rate of waning of vaccine-induced effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 3 or 10 years\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding Statement\u003c/h2\u003e \u003cp\u003eAmy Dighe acknowledges funding from the Wellcome Trust Studentship 203871/Z/16/Z. All authors acknowledge funding from the Medical Research Council (MRC) Centre for Global Infectious Disease Analysis (MR/X020258/1) funded by the UK MRC and carried out in the frame of the Global Health EDCTP3 Joint Undertaking supported by the EU; the NIHR for support for the Health Research Protection Unit in Modelling and Health Economics, a partnership between the UK Health Security Agency (UKHSA), Imperial College London, and London School of Hygiene \u0026amp; Tropical Medicine (grant code NIHR200908); a philanthropic donation from Community Jameel supporting the work of the Jameel Institute. The funders had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. For the purpose of open access, the author has applied a \u0026lsquo;Creative Commons Attribution\u0026rsquo; (CC BY) licence to any Author Accepted Manuscript version arising from this submission.\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eConceptualisation: NF, AD, and TJ, formal analysis: AD, methodology: NF, AD and TJ, manuscript writing \u0026ndash; original draft AD, writing - review and editing NF and TJ.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors acknowledge helpful input from Maria Van Kerkhove in shaping the focus of this work.\u003c/p\u003e\u003ch2\u003eData and code availability\u003c/h2\u003e \u003cp\u003eData and code used to generate the results described in this paper are available here: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/AmyDighe/mers-cov-camels\u003c/span\u003e\u003cspan address=\"https://github.com/AmyDighe/mers-cov-camels\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLessler J et al (2016) Estimating the Severity and Subclinical Burden of Middle East Respiratory Syndrome Coronavirus Infection in the Kingdom of Saudi Arabia. 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A.\u003c/em\u003e 118\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbbas B, Al Qarawi AA, Al Hawas A (2000) Survey on camel husbandry in Qassim region, Saudi Arabia: Herding strategies, productivity and mortality. Rev \u0026Eacute;lev M\u0026eacute;d V\u0026eacute;t Pays Trop 53:293\u0026ndash;298\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiguel E et al (2017) Risk factors for MERS coronavirus infection in camels in Burkina Faso, Ethiopia, and Morocco, 2015. \u003cem\u003eEurosurveillance\u003c/em\u003e 22\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGossner C et al (2016) Human-Camel Interactions and the Risk of Acquiring Zoonotic Middle East Respiratory Syndrome Coronavirus Infection. Zoonoses Public Health 63:1\u0026ndash;9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMirkena T et al (2018) Camel production systems in Ethiopia: a review of literature with notes on MERS-CoV risk factors. 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Emerg Infect Dis 20:2093\u0026ndash;2095\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOIE, Chapter (2021) 3.5.2 Middle East Respiratory Syndrome (Infection of Camels with Middle East Respiratory Syndrome Coronavirus). \u003cem\u003eOIE Terrestrial Manual\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuench H (1934) Derivation of Rates from Summation Data by the Catalytic Curve. J Am Stat Assoc 29:25\u0026ndash;38\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHens N et al (2010) Seventy-five years of estimating the force of infection from current status data. Epidemiol Infect 138:802\u0026ndash;812\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStan Development Team. RStan: the R interface to Stan. R package version 2.17.3. (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A (2002) Bayesian measures of model complexity and fit. J R Stat Soc Ser B Stat Methodol 64:583\u0026ndash;639\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdallah HR, Faye B (2013) Typology of camel farming system in Saudi Arabia. Emir J Food Agric 25:250\u0026ndash;260\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKasem S et al (2018) Cross-sectional study of MERS-CoV-specific RNA and antibodies in animals that have had contact with MERS patients in Saudi Arabia. J Infect Public Health 11:331\u0026ndash;338\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlotDigitizer. PlotDigitizer: Extract Data from Graph Image Online (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdussamad AM, Holtz W, Gauly M, Sulieman MS, Bello MB (2011) Reproduction and breeding in camels: insights from pastoralists in some selected villages of the Nigeria-Niger corridor. 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J Anim Poult Prod 11:573\u0026ndash;581\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGikonyo S et al (2018) Mapping Potential Amplification and Transmission Hotspots for MERS-CoV, Kenya. \u003cem\u003eEcoHealth\u003c/em\u003e 15, 372\u0026ndash;387\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team. R: A Language and Environment for Statistical Computing (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFitzJohn R, Fischer T (2022) odin: ODE generation and Integration\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFitzJohn R, Lees J (2022) odin.dust: Compile Odin to Dust\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLees JA et al (2021) Reproducible parallel inference and simulation of stochastic state space models using odin, dust, and mcstate. Wellcome Open Res 5\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"MERS-CoV, camels, vaccination, mathematical modelling, zoonotic disease prevention","lastPublishedDoi":"10.21203/rs.3.rs-5342913/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5342913/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOutbreaks of Middle East Respiratory syndrome coronavirus (MERS-CoV) in humans are driven by recurring zoonotic spillover from camels, leading to demand for camel vaccination. With two vaccine candidates shown to reduce infectiousness, there is a need to better understand transmission of MERS-CoV in camels and assess the potential impact of vaccination. To help address this, we used age-stratified seroprevalence data and a combination of modelling methodologies to estimate key epidemiological quantities including MERS-CoV transmissibility in camels and to estimate vaccine impact on infection incidence. Transmissibility was higher in the Middle East (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e range 3\u0026ndash;34) compared to Africa (2\u0026ndash;15) and South Asia (2\u0026ndash;4), highlighting the need for setting-specific vaccination strategies. Modelling suggested that even if the vaccine only reduced infectiousness rather than susceptibility to infection, vaccinating calves could achieve large reductions in incidence in moderate and high transmission settings, and interrupt transmission in low transmission settings, provided coverage was high (70\u0026ndash;90%).\u003c/p\u003e","manuscriptTitle":"Modelling transmission of Middle East respiratory syndrome coronavirus in camel populations and the potential impact of animal vaccination","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-04 04:45:15","doi":"10.21203/rs.3.rs-5342913/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6098a0b1-6518-434d-bca7-fa4c30a21bf0","owner":[],"postedDate":"November 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":39662156,"name":"Health sciences/Diseases/Infectious diseases/Viral infection"},{"id":39662157,"name":"Health sciences/Diseases/Respiratory tract diseases"}],"tags":[],"updatedAt":"2025-08-19T07:07:08+00:00","versionOfRecord":{"articleIdentity":"rs-5342913","link":"https://doi.org/10.1038/s41467-025-62365-x","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2025-08-18 04:00:00","publishedOnDateReadable":"August 18th, 2025"},"versionCreatedAt":"2024-11-04 04:45:15","video":"","vorDoi":"10.1038/s41467-025-62365-x","vorDoiUrl":"https://doi.org/10.1038/s41467-025-62365-x","workflowStages":[]},"version":"v1","identity":"rs-5342913","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5342913","identity":"rs-5342913","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}