Impact of a six-month COVID-19 travel moratorium on Plasmodium falciparum malaria prevalence on Bioko Island, Equatorial Guinea

Impact of a six-month COVID-19 travel moratorium on Plasmodium falciparum malaria prevalence on Bioko Island, Equatorial Guinea | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Impact of a six-month COVID-19 travel moratorium on Plasmodium falciparum malaria prevalence on Bioko Island, Equatorial Guinea Dianna Hergott, Carlos Guerra, Guillermo García, Jeremías Nzamio, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4189942/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Sep, 2024 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Importation of malaria infections has long been suspected as a driver of sustained malaria prevalence on areas of Bioko Island, Equatorial Guinea. However, quantifying the impact of imported infections is difficult because of the dynamic nature of the disease and the complexity of designing a randomized trial. Here, we leverage a six-month travel moratorium in and out of Bioko Island during the initial COVID-19 pandemic response to evaluate the contribution of imported infections to Pf prevalence on Bioko Island. Using a difference in differences design and data from island wide household surveys conducted before (2019) and after (2020) the travel moratorium, we compared the change in prevalence between areas of low historical travel to those with high historical travel. We found that prevalence increased in low travel areas after the moratorium compared to before, while prevalence decreased in high travel areas. In the absence of a travel moratorium, the prevalence of infection in high travel areas was expected to be 5% higher than what was observed. The observed decrease in prevalence can be directly attributed to the lack of imported cases, highlighting the importance of control measures that target these types of infections. Health sciences/Risk factors Health sciences/Diseases/Infectious diseases/Malaria Figures Figure 1 Figure 2 Introduction Despite increased efforts and control strategies, Plasmodium falciparum (Pf) malaria remains endemic in 85 countries and territories. 1 The increased frequency in which individuals move between and within countries has created added challenges for areas that have recently eliminated malaria, as well as those working towards elimination. 2–10 Bioko Island, Equatorial Guinea, has seen a significant decrease in malaria burden over the past two decades; 11 however, there are several areas of the island, especially urban areas where prevalence is typically lower, that have not yet approached pre-elimination levels. These urban areas also tend to have a higher proportion of individuals who travel between Bioko Island and the mainland of Equatorial Guinea, 12 where malaria prevalence is substantially higher. 7 Several previous studies suggest that there is a high amount of importation of malaria to Bioko Island in returning travelers 13 , and that these imported infections contribute to sustained prevalence in urban areas. 7,14 However, previous analyses are based on retrospective reporting of travel history captured via cross-sectional surveys, and there have been no studies that have allowed for direct estimation of the impact of imported malaria cases to prevalence in high travel areas. Better estimates of the contribution of imported cases to malaria transmission are needed to inform malaria control measures. In 2020, to address the COVID-19 pandemic and minimize local transmission of SARS-CoV-2, Equatorial Guinea imposed travel restrictions throughout the country, eliminating movement between the islands and the mainland from March to September 2020. 15 This, ostensibly, also eliminated the importation of Plasmodium infections to Bioko. The travel restriction provides a natural experiment in which the impact of imported infections can be directly assessed. Here, we compare the prevalence and odds of malaria infection before and after the travel restriction in areas that historically have had a high-volume of travel to areas of historically low volume of travel using a difference in differences analysis. This allows us to directly assess the impact of imported malaria infection on prevalence on Bioko Island. Data on malaria infection was collected before (2019) and after (2020) the travel moratorium through a household-based malaria indicator survey (MIS) with households selected from the whole island. 16,17 Using historical reported travel from MIS between 2015 and 2018, we used the distribution of travel frequency by enumeration area (EA) to classify areas as “high travel” (in the top quartile) or “low travel” (bottom quartile). We then compared the change in malaria prevalence in high and low travel areas before and after the travel mortarium to estimate the contribution of imported malaria to prevalence in high travel areas. Results Travel Prevalence and Sample Selection In 2019 and 2020, there were 109 EAs sampled in the MIS. Based on 2015–2018 data, the range of travel prevalence to the mainland of Equatorial Guinea in each EA ranged from 1.5–39.9%. We classified the EAs in the top quartile of the travel distribution (≥ 12.2%) as high travel areas, and those in the bottom quartile of the travel distribution (≤ 4.3%) as low travel areas, resulting in 56 EAs classified as high or low travel areas included in this analysis (Fig. 1 ). The distribution of households and individuals in each of the travel classifications is shown in Table 1 . Low travel areas had fewer individuals sampled compared to high travel areas, consistent with the population densities of these areas. Table 1 Total enumeration areas (EA), households, and individuals sampled in the Bioko Island Malaria Indicator Survey and included in the difference in differences, stratified by travel area and stratum for 2019 and 2020. Low Travel Areas High Travel Areas Stratum 1 n (%) Stratum 2 n (%) Total n Stratum 1 n (%) Stratum 2 n (%) Total n Enumeration Areas 17 (61) 11 (39) 28 5 ( 18 ) 23 (82) 28 2019 Households Sampled 498 (62) 305 (38) 803 179 ( 12 ) 1356 (88) 1535 Individuals Sampled 1334 (57) 1021 (43) 2355 457 ( 10 ) 4079 (90) 4536 2020 Households Sampled 468 (64) 268 (36) 736 147 ( 11 ) 1226 (89) 1373 Individuals Sampled 1110 (57) 848 (43) 1958 450 ( 10 ) 3896 (90) 4346 Malaria Prevalence before and after travel restrictions The unadjusted prevalence of malaria in 2019 was 7.5% in low travel areas (95% CI: 4.5, 10.5), and 13.8% in high travel areas (95% CI: 12.5, 15.1). In 2020, prevalence in low travel areas increased to 12.8% (95% CI: 6.9, 18.7) while decreasing to 11.9% in high travel areas (95% CI: 10.2, 13.5), representing a prevalence difference of 5.3% (95% CI: 0.3, 10.2), and − 1.9% (95% CI: -3.3, -0.6), respectively (Table 2 ). Assuming parallel trends in high and low travel areas, the Pf prevalence difference in high travel areas was 7.2% lower than would have been expected in the absence of the travel moratorium (95% CI: -12%, -2.2%), without adjustment. Table 2 Number of individuals tested, and prevalence estimate by year and travel group. Difference in prevalence compares 2020 to 2019 prevalence by travel area. The difference in differences compares to two prevalence differences. Group and Year Individuals Tested (n) Pf Prevalence (95% CI) Difference in Prevalence (95% CI) Difference in Differences (95% CI) Unadjusted Adjusted ± Unadjusted Adjusted ± Low travel − 2019 3182 7.5% (4.5, 10.5) 5.3 (0.6,10) 2.1 (-1.1,5.3) -7.2 (-12.1,-2.3) -5.1 (-8.9,-1.3) Low travel − 2020 1178 12.8% (6.9, 18.7) High travel − 2019 7070 13.8% (12.5, 15.1) -1.9 (-3.2,-0.6) -3.0 (-4.7,-1.3) High travel − 2020 1674 11.9% (10.3, 13.5) ± Adjusted for coverage of Indoor Residual Spraying (IRS) and going indoors before 7PM. A variety of factors known to be related to malaria risk were compared between 2019 and 2020 within each of the travel groups ( Supplementary Table 1). Of those evaluated, only the time that individuals went indoors, and the insecticide spray coverage showed significant differences between years and were included in the final model. The proportion of individuals who reported going inside their house before 7pm doubled from 2019 to 2020 in both low travel areas (22.5–47.2%) and high travel areas (28.1% vs. 50.4%). The proportion of households sprayed with insecticide decreased in 2020 (35.1%) compared to 2019 (46.9%) in low travel areas, whereas the spray coverage in high travel areas was higher in 2020 compared to 2019 (57.2% versus 28.3%), consistent with the targeted IRS approach of the program. 18 When adjusting for spray coverage and going inside before 7pm, prevalence in low travel areas increased by 2.1% (95% CI:- 1.1, 5.3) between 2019 and 2020, but decreased in high travel areas by 3.0% (95% CI: -4.7, -1.3) over the same period. After adjustment, the Pf prevalence in high travel areas was 5.1% lower than would have been expected in the absence of the travel moratorium (95% CI: -8.9%, -1.3%). Similar results were seen when evaluating the relationship on a relative scale. Comparing the change from 2019 to 2020 in high travel areas to low travel areas, the unadjusted odds of Pf infection after the travel moratorium were 51% lower in high travel areas (OR = 0.49, 95% CI: 0.30, 0.73) than would be expected. Following adjustment, the difference was diminished but still significant, with odds of infection 39% lower in high travel areas compared to what would have been expected based on trends in low travel areas (aOR = 0.61; 95% CI: 0.43, 0.88). Full results are shown in Supplementary Table 5 and Supplementary Fig. 2 . Sensitivity analysis There were three areas on the island, composed of seven EAs, with known land use changes over the study period that may have impacted risk of malaria transmission, outlined by orange in Fig. 1 . Three of these EAs on the western coast were included in our analysis. Evaluation of estimated prevalence in 2019 and 2020 in these three areas showed that in two of the three EAs, the observed prevalence difference between years was greater than the average difference in the low travel group ( Supplementary Table 3 ). When we removed these three EAs from the analysis, the Pf prevalence difference in high travel areas was 5.2% lower than would have been expected in the absence of the travel moratorium (95% CI: -8.9, -1.5) in the unadjusted analysis and 4.6% lower than expected in the adjusted analysis (95% CI: -8.1, -1.1) ( Supplementary Table 4 ). Comparing the relative change from 2019 to 2020 in high travel areas to low travel areas, the adjusted odds of Pf infection after the travel moratorium were 32% lower in high travel areas (aOR = 0.68, 95% CI: 0.44, 0.92) ( Supplementary Table 5 ). For the results of a difference in differences model to be valid, the parallel trends assumption must hold. 19 Using data from 2018, the mean prevalence in high and low travel EAs was calculated and plotted with unweighted data from 2019 and 2020. Figure 3 indicates that the trend from 2018 to 2019 was similar in low and high travel groups, and then diverged in 2020 when travel was stopped. A model of the Pf positivity for 2018 and 2019 among high and low travel areas, with an interaction term for year and travel showed no significant difference in the change in prevalence between the two areas (p = 0.29), and plot of the mean residuals were parallel ( Supplementary Fig. 1 ). Discussion Simulation models suggest there are areas of Bioko Island in which high proportions of malaria prevalence can be attributed to infections acquired while traveling to higher burden areas. 7,13,14 A recent model suggested that drastically reducing the number of imported infections between Bioko Island and the mainland could significantly reduce prevalence in areas with a high proportion of travelers. 14 However, prior to 2020, there were, understandably, no intervention studies nor other data to definitively support the model simulations. Travel restrictions imposed as a measure to control the spread of SARS-CoV-2 in 2020 provided an opportunity to directly evaluate the impact of imported infections. We observed that in the absence of travel, prevalence in historically high travel areas decreased by 2%, while prevalence in low travel areas increased by 5% over the same period. This suggests that, assuming parallel trends, in the absence of the travel moratorium, one would have expected Pf prevalence to be 7% higher in high travel areas than what was observed. When adjusting for spray coverage and time one went indoors, the difference in trends decreased to 5% but remained significant. In 2019, prior to the travel restrictions, odds of malaria infection were two to three times higher in areas of Bioko Island with a historically high proportion of travelers, relative to areas of historically low travel. This finding is similar to a 2013 analysis that showed infection risk was greater in children living in areas with the highest proportion of travelers. 13 In 2020, when the movement of individuals was restricted, there was no difference in risk of malaria infection observed in high travel areas compared to low travel areas. This observation both further supports the hypothesis that a significant fraction of the Pf prevalence observed in high travel areas could be explained by imported infections, 7 while also suggesting that malaria risk in these areas is not solely driven by importation. Previous analyses have suggested there may be areas where malaria prevalence is solely attributable to imported infection, 7 as several of these locations are in urban centers where there is generally improved infrastructure and fewer mosquitoes. If this were true, it would have been anticipated that the risk in high travel areas would be substantially less relative to low travel areas once importation was eliminated. However, this analysis showed no difference in odds of infection in the absence of travel. One explanation for this finding is that there was still residual travel occurring, even with the travel moratorium, which allowed infections to continue to be imported during 2020. While a small percentage of individuals did report travel in the past 8 weeks in the 2020 MIS (1%), it seems unlikely that this would sustain the observed prevalence in the population of high travel areas. Another explanation is that infections in high travel areas were acquired through travel to other areas of the island. There is often frequent travel within the island, especially between Malabo and areas in the periphery, where the force of infection is significantly higher. 12,14 In 2019, residents in both high and low travel areas reported an average of 1 trip to another part of the island in the past 8 weeks (range: 1–5 trips), which did not substantially change in 2020. Therefore, it is possible that the remaining prevalence in high travel areas is from within-island parasite movement. However, there was no significant difference seen between prevalence in those who reported within island travel and those who did not in 2020 ( Supplementary Table 1 ), suggesting this does not offer a full explanation. A final possibility is that high travel areas are receptive to local transmission, and levels of endemic transmission persist even when imported infections are removed. This is supported by a 2019 incidence study conducted in Malabo, which suggests local transmission is occurring in peri-urban areas in Malabo district. 20 In that study, while travelers tended to be more likely to have an infection, the no incident infections related to travel were identified, supporting the hypothesis that there is local transmission occurring, even in areas where travel is common. In addition, recent entomological monitoring in urban Malabo using human landing catches 21 and larval collections have confirmed the presence of anopheline vectors showing varying levels of human biting rates and larval densities across the city ( Supplementary Fig. 3 ). Therefore, the results of this analysis suggest that control strategies that aim to reduce the malaria burden in travelers, either by reducing the burden in the areas where they travel, and/or by treating returning travelers, would impact the overall prevalence in several communities in Bioko. Additionally, control measures that aim to reduce local transmission, such as IRS, distribution of LLINs, and larval source management should be continued, even if additional interventions that target imported infections are introduced. Further analyses are needed to better understand the role of importation and local transmission at a more granular level. The interpretation of our results depends on several assumptions. First, it is assumed that the change in prevalence in low-travel areas is a valid estimate of the change we would expect in the high-travel areas in the absence of imported infections. That is, the two areas had parallel trends prior to the elimination of travel. 19 While it is difficult to definitively verify this assumption, comparing data from the 2018 MIS to that from 2019 and 2020 suggested that prior to the halt in travel, there were similar trends over time in the high and low travel groups. Additionally, the final model adjusted for measured variables that impact malaria transmission and changed over time within travel groups (time variant factors). However, it is possible that there are unmeasured factors that were not accounted for in the model. Most notable would be changes in the ecological landscape. For example, an outbreak occurred in 2019 in a low-travel area in the south of the Island, because of recent construction that created additional breeding sites. 22 There was no precise measure of land use over the study period, so it was not possible to adjust for this variable in the analysis. However, there were seven EAs known to have major changes in land use, including the one where the 2019 outbreak occurred, and one where an outbreak occurred in 2020. Three of these EAs were in our analysis, and two areas showed large prevalence differences from 2019 to 2020, suggesting there may have been additional transmission due to the changes in the ecological landscape. When these EAs were removed from the analysis, the difference in differences and ratio of ratios were slightly attenuated, but still of similar magnitude and significance. Therefore, if it were possible to precisely measure changes in the ecological landscape and include them in the model, we may expect a slightly lower prevalence difference, but the conclusions would remain the same. Another assumption of a difference-in-differences model is that secular trends are consistent over time and have the same impact on both travel areas. While there have been changes in the monthly amount of rainfall overtime on Bioko, there have been increases both in high and low travel areas, and the assumption is that this would equally impact malaria transmission potential in these areas. The emergence of SARS-CoV-2 in early 2020 disrupted health systems around the world. As countries closed borders, limited movement, and restricted activities to curtail the initial spread of COVID-19, other public health programs were impacted. This is especially true for many malaria endemic countries, in which COVID-19 restrictions and global supply chain issues resulted in disruptions in the distribution of long-lasting insecticide nets, application of insecticides, and availability of anti-malarial medicines. 15,23,24 The World Health Organization modeled the potential impact of disruptions to malaria interventions and estimated these disruptions could increase cases by upwards of 20% and deaths by greater than 50%, especially in scenarios where access to treatment was disrupted. 25 Similar impacts were seen during the Ebola outbreak in 2014–2015 when health systems were disrupted. 26–29 However, in these models and analyses, the potential impact of reducing importation and movement of Plasmodium infections was not considered. 30,31 This analysis shows that on Bioko Island, where malaria control interventions remained largely uninterrupted during the pandemic 15 travel restrictions resulted in a decrease in malaria prevalence in areas with high travelers. It is possible that other areas with high proportions of imported infections may also have seen these decreases because of the travel restrictions, despite other interruptions to the health care system. This analysis suggests that the impact of COVID-19 on malaria burden may be underestimated in areas with a high prevalence of travelers. Additionally, as borders are now open and imported infections return, malaria control strategy discussions should include interventions that target these infections to reduce burden. Conclusions Travel restrictions initiated to limit the spread of COVID-19 allowed for a direct quantification of the impact of imported Plasmodium infections to malaria risk on Bioko Island. This analysis demonstrated that there is a substantial proportion of malaria risk that is attributed to the importation of infections. However, even without imported parasites, there is evidence of local transmission. Therefore, strategies that aim to reduce local transmission as well as limit the number of imported infections should be considered to continue to drive the reduction of malaria burden on Bioko Island. Methods Malaria Indicator Survey Structure The MIS is carried out annually on Bioko Island between August and September has been previously described. 16,17 Briefly, information on malaria risk factors, including off island travel in the previous eight weeks, is collected from selected households. Sampling units are geographically defined enumeration areas (EAs); under this scheme all households were eligible for selection into the survey through a stratified, single-cluster survey design. To guide and track programmatic malaria activities, Bioko Island has been divided into geographically defined “map areas” which are 1 km x 1 km squares. 32 The 209 map areas that included households were used to define EAs for the MIS. If a map area had at least 100 households, it was its own EA; if there were fewer than 100 households in the map area, several map areas were combined (based on geographical proximity) to create an EA with at least 100 households. Before sampling, the EAs were then divided into two strata based on population density and estimated local residual transmission (LRT), which is the predicted amount of infections acquired locally. 7 To select the sample for the MIS, within each EA, a simple random sample of households was taken using specified sampling fractions for each stratum: 24% for stratum 1, and 4.8% in stratum 2. All adults provided written consent for testing, and the head of household consented for anyone under the age of 18. All consenting individuals who lived in a selected household and were present during the time of the survey were tested for Plasmodium malaria parasites using a CareStart Malaria HRP2/pLDH rapid diagnostic test (RDT) (Access Bio, Somerset, NJ, USA). Individuals who were positive for malaria by RDT were provided with artemisinin-combination therapy (ACT) by a Ministry of Health and Social Welfare (MoHSW) nurse per national policy, based on World Health Organization guidelines. 33 Sample selection for analysis Smoothed mainland travel prevalence (the fraction of people surveyed who reported having travelled to the mainland in the eight weeks prior to the survey) for each map area was estimated using travel data from the 2015 to 2018 MIS, as per methods described in Guerra et al . 7 For map areas with no estimates, the value from their nearest neighbor was utilized. If an EA was composed of multiple map areas, a weighted average was calculated from all map areas in the EA. The weight of each map area was equal to the number of households in that area out of the total number of households in the EA. After a historical travel prevalence was assigned to each EA, those in the top quartile of travel prevalence were labeled as “high travel” areas, and those in the bottom quartile of travel prevalence were labeled as “low travel” areas; EAs from the middle two quartiles were excluded from analysis ( Figure 3 ) . Statistical Analyses All analyses were conducted within the survey package of R (v3.6.2). The survey design dataset accounted for the stratified sampling weights of the original MIS as well as the non-independence of results within households and within EA. The main outcome of interest, Pf positivity, was coded as a binary variable. For each travel area, the survey mean prevalence was estimated from individual level data by year and are presented with a 95% CI. To analyze the possible impact of the travel moratorium on malaria risk on Bioko Island, a difference in differences analysis was conducted to compare the difference in prevalence of infection between 2019 and 2020 in historically high travel areas relative to the difference in prevalence in historically low travel areas during the same time. For our main analysis, we fit an unadjusted and adjusted survey generalized linear model with robust standard errors. To determine variables to include in the adjusted model, we compared values of several variables determined to be related to malaria risk a priori between 2019 and 2020 within travel group. Any variable that had a meaningful difference between years within a travel group was included in the final model. To estimate how the prevalence of infection in high travel areas changed between 2019 and 2020 relative to the change in prevalence in low travel areas over the same period, the model included an interaction term between a binary variable for time, and travel. Coefficients and 95% CIs were extracted for various combinations. Given that our outcome was binary, we also evaluated the relationship between odds of infection in high and low travel groups between years using logistic regression. The same models were fit, but utilizing a logit link function. Coefficients and 95% CIs were exponentiated to get comparative odds ratios between years and travel areas. Data from the 2018 MIS was used to assess the robustness of the parallel trends’ assumption by visually assessing the trends from 2018 to 2019 in high and low travel areas, fitting a linear model with an interaction term for year and travel stratum in the pre-moratorium data, and by plotting the mean residuals of a linear model regressing Pf prevalence over time. 34 For the analysis of parallel trends, non-survey weighted prevalence was calculated each year, as sample selection in 2018 was not done in the same manner as subsequent years. There were seven EAs known to have had large land use changes over the study period, three of which were in our analytic dataset. As there was not a reliable way to measure land use change in all areas during the study period, we conducted a sensitivity analysis, in which the main analysis was repeated with a data set that excluded the three EAs that were known to have had land use changes over the study period. 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Confounding and regression adjustment in difference-in-differences studies. Health Serv. Res. 56 , 932–941 (2021). Additional Declarations There is NO Competing Interest. Supplementary Files DiDsupplementaltablesandfigures.docx Cite Share Download PDF Status: Published Journal Publication published 27 Sep, 2024 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4189942","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":287860482,"identity":"37b9881c-fa28-4f43-8290-a52038530f44","order_by":0,"name":"Dianna Hergott","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYDCCG1CaH0yyEa/FQEKygWQtBgeI1cJ3u/nYxy8Vf+qMb+QYMHwoO0xYi+SdY8mzZc4YSJgBtTDOOEeEFoMbOcbMkm0gLbkbmHnbiNKS/xmsxXgGUMtf4rTkMDN+BGoxkABqYSRGi+SNNGNmhjPGkjPOvP9wsOdcOmEtfDeSHzP+qJDj529PS3zwo8yasBYQYOaBMg4Qpx4IGH8QrXQUjIJRMApGJAAApH87vqoYURsAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-9417-4340","institution":"University of Washington School of Public Health","correspondingAuthor":true,"prefix":"","firstName":"Dianna","middleName":"","lastName":"Hergott","suffix":""},{"id":287860483,"identity":"39ff291d-2c48-4326-b769-d0b9f63d7352","order_by":1,"name":"Carlos Guerra","email":"","orcid":"https://orcid.org/0000-0002-4069-9528","institution":"Medical Care Development International","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Guerra","suffix":""},{"id":287860484,"identity":"97620894-612e-4595-9289-1c2611e87e6d","order_by":2,"name":"Guillermo García","email":"","orcid":"","institution":"Medical Care Development International","correspondingAuthor":false,"prefix":"","firstName":"Guillermo","middleName":"","lastName":"García","suffix":""},{"id":287860485,"identity":"e58bcf2d-542e-474b-9d07-d95b91cc7fac","order_by":3,"name":"Jeremías Nzamio","email":"","orcid":"","institution":"Medical Care Development International","correspondingAuthor":false,"prefix":"","firstName":"Jeremías","middleName":"","lastName":"Nzamio","suffix":""},{"id":287860486,"identity":"bb3f2d63-4c6c-482c-95df-1b5629e7d786","order_by":4,"name":"Olivier Donfack","email":"","orcid":"","institution":"MCD Global Health","correspondingAuthor":false,"prefix":"","firstName":"Olivier","middleName":"","lastName":"Donfack","suffix":""},{"id":287860487,"identity":"48980e64-53df-4cbd-b666-42e8dadbfc9c","order_by":5,"name":"Marcos Mbulito Iyanga","email":"","orcid":"","institution":"MCD Global Health","correspondingAuthor":false,"prefix":"","firstName":"Marcos","middleName":"Mbulito","lastName":"Iyanga","suffix":""},{"id":287860488,"identity":"c7826229-5be1-42c9-831e-31b2ab7becad","order_by":6,"name":"Restituto Mba Nguema","email":"","orcid":"","institution":"MCD Global Health","correspondingAuthor":false,"prefix":"","firstName":"Restituto","middleName":"Mba","lastName":"Nguema","suffix":""},{"id":287860489,"identity":"b0890f15-812b-4853-b635-d6d243dd5436","order_by":7,"name":"Crisantos Nsue Abeso","email":"","orcid":"","institution":"MCD Global 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Washington","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Smith","suffix":""},{"id":287860496,"identity":"2502fc8e-24c2-4f11-ab71-c91998ef056f","order_by":14,"name":"Jennifer Balkus","email":"","orcid":"https://orcid.org/0000-0002-9950-2523","institution":"Fred Hutchinson Cancer Research Center","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Balkus","suffix":""}],"badges":[],"createdAt":"2024-03-29 23:15:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4189942/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4189942/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-024-52638-2","type":"published","date":"2024-09-27T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54319732,"identity":"a96904dc-3803-4ca5-b332-d2b08454fa1b","added_by":"auto","created_at":"2024-04-08 18:57:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":76172,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Bioko Island, Equatorial Guinea, showing enumeration areas (EAs) selected for the difference in differences analysis. EAs in teal are those in the top quartile of historical smoothed travel prevalence. EAs in gold are those in the bottom quartile of historical smoothed travel prevalence. EAs in grey were not selected for the analysis. EAs outlined in orange and filled with patterned dots are the areas with known land use changes over the study period.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4189942/v1/b1d680afdc646b6cfae3f3ec.png"},{"id":54319733,"identity":"cb8a72b3-af07-4bce-a60c-c67eecf3d16b","added_by":"auto","created_at":"2024-04-08 18:57:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":51677,"visible":true,"origin":"","legend":"\u003cp\u003eTest of parallel trends assumption. Estimated mean \u003cem\u003ePf \u003c/em\u003eprevalence in low travel (gold) and high travel (teal) areas in 2018, 2019 and 2020. The dashed line represents predicted prevalence in 2020 in a counterfactual scenario with no travel moratorium.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4189942/v1/7acca288c6b8e5633bb2c2a7.png"},{"id":65484398,"identity":"3d1c2c80-8749-4434-b459-a33f2628ee68","added_by":"auto","created_at":"2024-09-28 07:07:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":742147,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4189942/v1/3d3bca49-4173-4d3c-95e9-fa2c790009d9.pdf"},{"id":54319735,"identity":"ac403be7-50dc-4dab-89ec-91c6365d01fb","added_by":"auto","created_at":"2024-04-08 18:57:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":238697,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"DiDsupplementaltablesandfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-4189942/v1/49621c7aab7bbabdb4637bfc.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Impact of a six-month COVID-19 travel moratorium on Plasmodium falciparum malaria prevalence on Bioko Island, Equatorial Guinea","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDespite increased efforts and control strategies, \u003cem\u003ePlasmodium falciparum (Pf)\u003c/em\u003e malaria remains endemic in 85 countries and territories.\u003csup\u003e1\u003c/sup\u003e The increased frequency in which individuals move between and within countries has created added challenges for areas that have recently eliminated malaria, as well as those working towards elimination. \u003csup\u003e2\u0026ndash;10\u003c/sup\u003e Bioko Island, Equatorial Guinea, has seen a significant decrease in malaria burden over the past two decades;\u003csup\u003e11\u003c/sup\u003e however, there are several areas of the island, especially urban areas where prevalence is typically lower, that have not yet approached pre-elimination levels. These urban areas also tend to have a higher proportion of individuals who travel between Bioko Island and the mainland of Equatorial Guinea,\u003csup\u003e12\u003c/sup\u003e where malaria prevalence is substantially higher.\u003csup\u003e7\u003c/sup\u003e Several previous studies suggest that there is a high amount of importation of malaria to Bioko Island in returning travelers\u003csup\u003e13\u003c/sup\u003e, and that these imported infections contribute to sustained prevalence in urban areas.\u003csup\u003e7,14\u003c/sup\u003e However, previous analyses are based on retrospective reporting of travel history captured via cross-sectional surveys, and there have been no studies that have allowed for direct estimation of the impact of imported malaria cases to prevalence in high travel areas. Better estimates of the contribution of imported cases to malaria transmission are needed to inform malaria control measures.\u003c/p\u003e \u003cp\u003eIn 2020, to address the COVID-19 pandemic and minimize local transmission of SARS-CoV-2, Equatorial Guinea imposed travel restrictions throughout the country, eliminating movement between the islands and the mainland from March to September 2020.\u003csup\u003e15\u003c/sup\u003e This, ostensibly, also eliminated the importation of \u003cem\u003ePlasmodium\u003c/em\u003e infections to Bioko. The travel restriction provides a natural experiment in which the impact of imported infections can be directly assessed. Here, we compare the prevalence and odds of malaria infection before and after the travel restriction in areas that historically have had a high-volume of travel to areas of historically low volume of travel using a difference in differences analysis. This allows us to directly assess the impact of imported malaria infection on prevalence on Bioko Island.\u003c/p\u003e \u003cp\u003eData on malaria infection was collected before (2019) and after (2020) the travel moratorium through a household-based malaria indicator survey (MIS) with households selected from the whole island.\u003csup\u003e16,17\u003c/sup\u003e Using historical reported travel from MIS between 2015 and 2018, we used the distribution of travel frequency by enumeration area (EA) to classify areas as \u0026ldquo;high travel\u0026rdquo; (in the top quartile) or \u0026ldquo;low travel\u0026rdquo; (bottom quartile). We then compared the change in malaria prevalence in high and low travel areas before and after the travel mortarium to estimate the contribution of imported malaria to prevalence in high travel areas.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTravel Prevalence and Sample Selection\u003c/h2\u003e \u003cp\u003eIn 2019 and 2020, there were 109 EAs sampled in the MIS. Based on 2015\u0026ndash;2018 data, the range of travel prevalence to the mainland of Equatorial Guinea in each EA ranged from 1.5\u0026ndash;39.9%. We classified the EAs in the top quartile of the travel distribution (\u0026ge;\u0026thinsp;12.2%) as high travel areas, and those in the bottom quartile of the travel distribution (\u0026le;\u0026thinsp;4.3%) as low travel areas, resulting in 56 EAs classified as high or low travel areas included in this analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The distribution of households and individuals in each of the travel classifications is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Low travel areas had fewer individuals sampled compared to high travel areas, consistent with the population densities of these areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal enumeration areas (EA), households, and individuals sampled in the Bioko Island Malaria Indicator Survey and included in the difference in differences, stratified by travel area and stratum for 2019 and 2020.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eLow Travel Areas\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eHigh Travel Areas\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStratum 1\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStratum 2\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStratum 1\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStratum 2\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnumeration Areas\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHouseholds Sampled\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e468 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e268 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e736\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e147 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1226 (89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1373\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndividuals Sampled\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1110 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e848 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1958\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e450 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3896 (90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e4346\u003c/b\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 \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMalaria Prevalence before and after travel restrictions\u003c/h2\u003e \u003cp\u003eThe unadjusted prevalence of malaria in 2019 was 7.5% in low travel areas (95% CI: 4.5, 10.5), and 13.8% in high travel areas (95% CI: 12.5, 15.1). In 2020, prevalence in low travel areas increased to 12.8% (95% CI: 6.9, 18.7) while decreasing to 11.9% in high travel areas (95% CI: 10.2, 13.5), representing a prevalence difference of 5.3% (95% CI: 0.3, 10.2), and \u0026minus;\u0026thinsp;1.9% (95% CI: -3.3, -0.6), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Assuming parallel trends in high and low travel areas, the \u003cem\u003ePf\u003c/em\u003e prevalence difference in high travel areas was 7.2% lower than would have been expected in the absence of the travel moratorium (95% CI: -12%, -2.2%), without adjustment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of individuals tested, and prevalence estimate by year and travel group. Difference in prevalence compares 2020 to 2019 prevalence by travel area. The difference in differences compares to two prevalence differences.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroup and Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndividuals Tested (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ePf\u003c/em\u003e Prevalence (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eDifference in Prevalence (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eDifference in Differences (95% CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted\u003csup\u003e\u0026plusmn;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdjusted\u003csup\u003e\u0026plusmn;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow travel \u0026minus;\u0026thinsp;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5% (4.5, 10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5.3 (0.6,10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.1 (-1.1,5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e-7.2 (-12.1,-2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e-5.1 (-8.9,-1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow travel \u0026minus;\u0026thinsp;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.8% (6.9, 18.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh travel \u0026minus;\u0026thinsp;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.8% (12.5, 15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-1.9 (-3.2,-0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-3.0 (-4.7,-1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh travel \u0026minus;\u0026thinsp;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.9% (10.3, 13.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e\u0026plusmn;\u003c/sup\u003eAdjusted for coverage of Indoor Residual Spraying (IRS) and going indoors before 7PM.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA variety of factors known to be related to malaria risk were compared between 2019 and 2020 within each of the travel groups (\u003cb\u003eSupplementary Table\u0026nbsp;1).\u003c/b\u003e Of those evaluated, only the time that individuals went indoors, and the insecticide spray coverage showed significant differences between years and were included in the final model. The proportion of individuals who reported going inside their house before 7pm doubled from 2019 to 2020 in both low travel areas (22.5\u0026ndash;47.2%) and high travel areas (28.1% vs. 50.4%). The proportion of households sprayed with insecticide decreased in 2020 (35.1%) compared to 2019 (46.9%) in low travel areas, whereas the spray coverage in high travel areas was higher in 2020 compared to 2019 (57.2% versus 28.3%), consistent with the targeted IRS approach of the program.\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWhen adjusting for spray coverage and going inside before 7pm, prevalence in low travel areas increased by 2.1% (95% CI:- 1.1, 5.3) between 2019 and 2020, but decreased in high travel areas by 3.0% (95% CI: -4.7, -1.3) over the same period. After adjustment, the \u003cem\u003ePf\u003c/em\u003e prevalence in high travel areas was 5.1% lower than would have been expected in the absence of the travel moratorium (95% CI: -8.9%, -1.3%).\u003c/p\u003e \u003cp\u003eSimilar results were seen when evaluating the relationship on a relative scale. Comparing the change from 2019 to 2020 in high travel areas to low travel areas, the unadjusted odds of \u003cem\u003ePf\u003c/em\u003e infection after the travel moratorium were 51% lower in high travel areas (OR\u0026thinsp;=\u0026thinsp;0.49, 95% CI: 0.30, 0.73) than would be expected. Following adjustment, the difference was diminished but still significant, with odds of infection 39% lower in high travel areas compared to what would have been expected based on trends in low travel areas (aOR\u0026thinsp;=\u0026thinsp;0.61; 95% CI: 0.43, 0.88). Full results are shown in \u003cb\u003eSupplementary Table\u0026nbsp;5 and Supplementary Fig.\u0026nbsp;2\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eThere were three areas on the island, composed of seven EAs, with known land use changes over the study period that may have impacted risk of malaria transmission, outlined by orange in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Three of these EAs on the western coast were included in our analysis. Evaluation of estimated prevalence in 2019 and 2020 in these three areas showed that in two of the three EAs, the observed prevalence difference between years was greater than the average difference in the low travel group (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e). When we removed these three EAs from the analysis, the \u003cem\u003ePf\u003c/em\u003e prevalence difference in high travel areas was 5.2% lower than would have been expected in the absence of the travel moratorium (95% CI: -8.9, -1.5) in the unadjusted analysis and 4.6% lower than expected in the adjusted analysis (95% CI: -8.1, -1.1) (\u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e). Comparing the relative change from 2019 to 2020 in high travel areas to low travel areas, the adjusted odds of \u003cem\u003ePf\u003c/em\u003e infection after the travel moratorium were 32% lower in high travel areas (aOR\u0026thinsp;=\u0026thinsp;0.68, 95% CI: 0.44, 0.92) (\u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eFor the results of a difference in differences model to be valid, the parallel trends assumption must hold.\u003csup\u003e19\u003c/sup\u003e Using data from 2018, the mean prevalence in high and low travel EAs was calculated and plotted with unweighted data from 2019 and 2020. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e indicates that the trend from 2018 to 2019 was similar in low and high travel groups, and then diverged in 2020 when travel was stopped. A model of the \u003cem\u003ePf\u003c/em\u003e positivity for 2018 and 2019 among high and low travel areas, with an interaction term for year and travel showed no significant difference in the change in prevalence between the two areas (p\u0026thinsp;=\u0026thinsp;0.29), and plot of the mean residuals were parallel (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSimulation models suggest there are areas of Bioko Island in which high proportions of malaria prevalence can be attributed to infections acquired while traveling to higher burden areas.\u003csup\u003e7,13,14\u003c/sup\u003e A recent model suggested that drastically reducing the number of imported infections between Bioko Island and the mainland could significantly reduce prevalence in areas with a high proportion of travelers.\u003csup\u003e14\u003c/sup\u003e However, prior to 2020, there were, understandably, no intervention studies nor other data to definitively support the model simulations. Travel restrictions imposed as a measure to control the spread of SARS-CoV-2 in 2020 provided an opportunity to directly evaluate the impact of imported infections. We observed that in the absence of travel, prevalence in historically high travel areas decreased by 2%, while prevalence in low travel areas increased by 5% over the same period. This suggests that, assuming parallel trends, in the absence of the travel moratorium, one would have expected \u003cem\u003ePf\u003c/em\u003e prevalence to be 7% higher in high travel areas than what was observed. When adjusting for spray coverage and time one went indoors, the difference in trends decreased to 5% but remained significant.\u003c/p\u003e \u003cp\u003eIn 2019, prior to the travel restrictions, odds of malaria infection were two to three times higher in areas of Bioko Island with a historically high proportion of travelers, relative to areas of historically low travel. This finding is similar to a 2013 analysis that showed infection risk was greater in children living in areas with the highest proportion of travelers.\u003csup\u003e13\u003c/sup\u003e In 2020, when the movement of individuals was restricted, there was no difference in risk of malaria infection observed in high travel areas compared to low travel areas. This observation both further supports the hypothesis that a significant fraction of the \u003cem\u003ePf\u003c/em\u003e prevalence observed in high travel areas could be explained by imported infections,\u003csup\u003e7\u003c/sup\u003e while also suggesting that malaria risk in these areas is not solely driven by importation.\u003c/p\u003e \u003cp\u003ePrevious analyses have suggested there may be areas where malaria prevalence is solely attributable to imported infection,\u003csup\u003e7\u003c/sup\u003e as several of these locations are in urban centers where there is generally improved infrastructure and fewer mosquitoes. If this were true, it would have been anticipated that the risk in high travel areas would be substantially less relative to low travel areas once importation was eliminated. However, this analysis showed no difference in odds of infection in the absence of travel. One explanation for this finding is that there was still residual travel occurring, even with the travel moratorium, which allowed infections to continue to be imported during 2020. While a small percentage of individuals did report travel in the past 8 weeks in the 2020 MIS (1%), it seems unlikely that this would sustain the observed prevalence in the population of high travel areas. Another explanation is that infections in high travel areas were acquired through travel to other areas of the island. There is often frequent travel within the island, especially between Malabo and areas in the periphery, where the force of infection is significantly higher.\u003csup\u003e12,14\u003c/sup\u003e In 2019, residents in both high and low travel areas reported an average of 1 trip to another part of the island in the past 8 weeks (range: 1\u0026ndash;5 trips), which did not substantially change in 2020. Therefore, it is possible that the remaining prevalence in high travel areas is from within-island parasite movement. However, there was no significant difference seen between prevalence in those who reported within island travel and those who did not in 2020 (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e), suggesting this does not offer a full explanation. A final possibility is that high travel areas are receptive to local transmission, and levels of endemic transmission persist even when imported infections are removed. This is supported by a 2019 incidence study conducted in Malabo, which suggests local transmission is occurring in peri-urban areas in Malabo district.\u003csup\u003e20\u003c/sup\u003e In that study, while travelers tended to be more likely to have an infection, the no incident infections related to travel were identified, supporting the hypothesis that there is local transmission occurring, even in areas where travel is common. In addition, recent entomological monitoring in urban Malabo using human landing catches\u003csup\u003e21\u003c/sup\u003e and larval collections have confirmed the presence of anopheline vectors showing varying levels of human biting rates and larval densities across the city (\u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e). Therefore, the results of this analysis suggest that control strategies that aim to reduce the malaria burden in travelers, either by reducing the burden in the areas where they travel, and/or by treating returning travelers, would impact the overall prevalence in several communities in Bioko. Additionally, control measures that aim to reduce local transmission, such as IRS, distribution of LLINs, and larval source management should be continued, even if additional interventions that target imported infections are introduced. Further analyses are needed to better understand the role of importation and local transmission at a more granular level.\u003c/p\u003e \u003cp\u003eThe interpretation of our results depends on several assumptions. First, it is assumed that the change in prevalence in low-travel areas is a valid estimate of the change we would expect in the high-travel areas in the absence of imported infections. That is, the two areas had parallel trends prior to the elimination of travel.\u003csup\u003e19\u003c/sup\u003e While it is difficult to definitively verify this assumption, comparing data from the 2018 MIS to that from 2019 and 2020 suggested that prior to the halt in travel, there were similar trends over time in the high and low travel groups. Additionally, the final model adjusted for measured variables that impact malaria transmission and changed over time within travel groups (time variant factors). However, it is possible that there are unmeasured factors that were not accounted for in the model. Most notable would be changes in the ecological landscape. For example, an outbreak occurred in 2019 in a low-travel area in the south of the Island, because of recent construction that created additional breeding sites.\u003csup\u003e22\u003c/sup\u003e There was no precise measure of land use over the study period, so it was not possible to adjust for this variable in the analysis. However, there were seven EAs known to have major changes in land use, including the one where the 2019 outbreak occurred, and one where an outbreak occurred in 2020. Three of these EAs were in our analysis, and two areas showed large prevalence differences from 2019 to 2020, suggesting there may have been additional transmission due to the changes in the ecological landscape. When these EAs were removed from the analysis, the difference in differences and ratio of ratios were slightly attenuated, but still of similar magnitude and significance. Therefore, if it were possible to precisely measure changes in the ecological landscape and include them in the model, we may expect a slightly lower prevalence difference, but the conclusions would remain the same. Another assumption of a difference-in-differences model is that secular trends are consistent over time and have the same impact on both travel areas. While there have been changes in the monthly amount of rainfall overtime on Bioko, there have been increases both in high and low travel areas, and the assumption is that this would equally impact malaria transmission potential in these areas.\u003c/p\u003e \u003cp\u003eThe emergence of SARS-CoV-2 in early 2020 disrupted health systems around the world. As countries closed borders, limited movement, and restricted activities to curtail the initial spread of COVID-19, other public health programs were impacted. This is especially true for many malaria endemic countries, in which COVID-19 restrictions and global supply chain issues resulted in disruptions in the distribution of long-lasting insecticide nets, application of insecticides, and availability of anti-malarial medicines.\u003csup\u003e15,23,24\u003c/sup\u003e The World Health Organization modeled the potential impact of disruptions to malaria interventions and estimated these disruptions could increase cases by upwards of 20% and deaths by greater than 50%, especially in scenarios where access to treatment was disrupted.\u003csup\u003e25\u003c/sup\u003e Similar impacts were seen during the Ebola outbreak in 2014\u0026ndash;2015 when health systems were disrupted.\u003csup\u003e26\u0026ndash;29\u003c/sup\u003e However, in these models and analyses, the potential impact of reducing importation and movement of \u003cem\u003ePlasmodium\u003c/em\u003e infections was not considered.\u003csup\u003e30,31\u003c/sup\u003e This analysis shows that on Bioko Island, where malaria control interventions remained largely uninterrupted during the pandemic\u003csup\u003e15\u003c/sup\u003e travel restrictions resulted in a decrease in malaria prevalence in areas with high travelers. It is possible that other areas with high proportions of imported infections may also have seen these decreases because of the travel restrictions, despite other interruptions to the health care system. This analysis suggests that the impact of COVID-19 on malaria burden may be underestimated in areas with a high prevalence of travelers. Additionally, as borders are now open and imported infections return, malaria control strategy discussions should include interventions that target these infections to reduce burden.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTravel restrictions initiated to limit the spread of COVID-19 allowed for a direct quantification of the impact of imported \u003cem\u003ePlasmodium\u003c/em\u003e infections to malaria risk on Bioko Island. This analysis demonstrated that there is a substantial proportion of malaria risk that is attributed to the importation of infections. However, even without imported parasites, there is evidence of local transmission. Therefore, strategies that aim to reduce local transmission as well as limit the number of imported infections should be considered to continue to drive the reduction of malaria burden on Bioko Island.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eMalaria Indicator Survey Structure\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe MIS is carried out annually on Bioko Island between August and September \u0026nbsp;has been previously described.\u003csup\u003e16,17\u003c/sup\u003e Briefly, information on malaria risk factors, including off island travel in the previous eight weeks, is collected from selected households. Sampling units are geographically defined enumeration areas (EAs); under this scheme all households were eligible for selection into the survey through a stratified, single-cluster survey design. To guide and track programmatic malaria activities, Bioko Island has been divided into geographically defined \u0026ldquo;map areas\u0026rdquo; which are 1 km x 1 km squares.\u003csup\u003e32\u003c/sup\u003e The 209 map areas that included households were used to define EAs for the MIS. If a map area had at least 100 households, it was its own EA; if there were fewer than 100 households in the map area, several map areas were combined (based on geographical proximity) to create an EA with at least 100 households. Before sampling, the EAs were then divided into two strata based on population density and estimated local residual transmission (LRT), which is the predicted amount of infections acquired locally.\u003csup\u003e7\u003c/sup\u003e To select the sample for the MIS, within each EA, a simple random sample of households was taken using specified sampling fractions for each stratum: 24% for stratum 1, and 4.8% in stratum 2.\u003c/p\u003e\n\u003cp\u003eAll adults provided written consent for testing, and the head of household consented for anyone under the age of 18. All consenting individuals who lived in a selected household and were present during the time of the survey were tested for \u003cem\u003ePlasmodium\u003c/em\u003e malaria parasites using a CareStart Malaria HRP2/pLDH rapid diagnostic test (RDT) (Access Bio, Somerset, NJ, USA). Individuals who were positive for malaria by RDT were provided with artemisinin-combination therapy (ACT) by a Ministry of Health and Social Welfare (MoHSW) nurse per national policy, based on World Health Organization guidelines.\u003csup\u003e33\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSample selection for analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSmoothed mainland travel prevalence (the fraction of people surveyed who reported having travelled to the mainland in the eight weeks prior to the survey) for each map area was estimated using travel data from the 2015 to 2018 MIS, as per methods described in Guerra \u003cem\u003eet al\u003c/em\u003e.\u003csup\u003e7\u003c/sup\u003e For map areas with no estimates, the value from their nearest neighbor was utilized. If an EA was composed of multiple map areas, a weighted average was calculated from all map areas in the EA. The weight of each map area was equal to the number of households in that area out of the total number of households in the EA.\u003c/p\u003e\n\u003cp\u003eAfter a historical travel prevalence was assigned to each EA, those in the top quartile of travel prevalence were labeled as \u0026ldquo;high travel\u0026rdquo; areas, and those in the bottom quartile of travel prevalence were labeled as \u0026ldquo;low travel\u0026rdquo; areas; EAs from the middle two quartiles were excluded from analysis (\u003cstrong\u003eFigure 3\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical Analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses were conducted within the \u003cem\u003esurvey\u003c/em\u003e package of R (v3.6.2). The survey design dataset accounted for the stratified sampling weights of the original MIS as well as the non-independence of results within households and within EA. The main outcome of interest, \u003cem\u003ePf\u0026nbsp;\u003c/em\u003epositivity, was coded as a binary variable. For each travel area, the survey mean prevalence was estimated from individual level data by year and are presented with a 95% CI.\u003c/p\u003e\n\u003cp\u003eTo analyze the possible impact of the travel moratorium on malaria risk on Bioko Island, a difference in differences analysis was conducted to compare the difference in prevalence of infection between 2019 and 2020 in historically high travel areas relative to the difference in prevalence in historically low travel areas during the same time. For our main analysis, we fit an unadjusted and adjusted survey generalized linear model with robust standard errors. To determine variables to include in the adjusted model, we compared values of several variables determined to be related to malaria risk \u003cem\u003ea priori\u003c/em\u003e between 2019 and 2020 within travel group. Any variable that had a meaningful difference between years within a travel group was included in the final model. To estimate how the prevalence of infection in high travel areas changed between 2019 and 2020 relative to the change in prevalence in low travel areas over the same period, the model included an interaction term between a binary variable for time, and travel. Coefficients and 95% CIs were extracted for various combinations.\u003c/p\u003e\n\u003cp\u003eGiven that our outcome was binary, we also evaluated the relationship between odds of infection in high and low travel groups between years using logistic regression. The same models were fit, but utilizing a logit link function. Coefficients and 95% CIs were exponentiated to get comparative odds ratios between years and travel areas.\u003c/p\u003e\n\u003cp\u003eData from the 2018 MIS was used to assess the robustness of the parallel trends\u0026rsquo; assumption by visually assessing the trends from 2018 to 2019 in high and low travel areas, fitting a linear model with an interaction term for year and travel stratum in the pre-moratorium data, and by plotting the mean residuals of a linear model regressing \u003cem\u003ePf\u003c/em\u003e prevalence over time.\u003csup\u003e34\u003c/sup\u003e For the analysis of parallel trends, non-survey weighted prevalence was calculated each year, as sample selection in 2018 was not done in the same manner as subsequent years.\u003c/p\u003e\n\u003cp\u003eThere were seven EAs known to have had large land use changes over the study period, three of which were in our analytic dataset. As there was not a reliable way to measure land use change in all areas during the study period, we conducted a sensitivity analysis, in which the main analysis was repeated with a data set that excluded the three EAs that were known to have had land use changes over the study period.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch1\u003eData Availability\u003c/h1\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch1\u003eAuthor Contributions\u003c/h1\u003e\n\u003cp\u003eRemoved for blinding\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWorld Health Organization. \u003cem\u003eWorld Malaria Report 2023\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eSturrock, H. J. W., Roberts, K. W., Wegbreit, J., Ohrt, C. \u0026amp; Gosling, R. D. Tackling Imported Malaria: An Elimination Endgame. \u003cem\u003eAm. J. Trop. Med. 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A. \u003cem\u003eet al.\u003c/em\u003e Mapping and enumerating houses and households to support malaria control interventions on Bioko Island. \u003cem\u003eMalar. J.\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 283 (2019).\u003c/li\u003e\n \u003cli\u003eWorld Health Organization. \u003cem\u003eGuidelines for the Treatment of Malaria.\u003c/em\u003e (2015).\u003c/li\u003e\n \u003cli\u003eZeldow, B. \u0026amp; Hatfield, L. A. Confounding and regression adjustment in difference-in-differences studies. \u003cem\u003eHealth Serv. Res.\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e, 932\u0026ndash;941 (2021).\u003c/li\u003e\n\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":"","lastPublishedDoi":"10.21203/rs.3.rs-4189942/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4189942/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eImportation of malaria infections has long been suspected as a driver of sustained malaria prevalence on areas of Bioko Island, Equatorial Guinea. However, quantifying the impact of imported infections is difficult because of the dynamic nature of the disease and the complexity of designing a randomized trial. Here, we leverage a six-month travel moratorium in and out of Bioko Island during the initial COVID-19 pandemic response to evaluate the contribution of imported infections to \u003cem\u003ePf \u003c/em\u003eprevalence on Bioko Island. Using a difference in differences design and data from island wide household surveys conducted before (2019) and after (2020) the travel moratorium, we compared the change in prevalence between areas of low historical travel to those with high historical travel. We found that prevalence increased in low travel areas after the moratorium compared to before, while prevalence decreased in high travel areas. In the absence of a travel moratorium, the prevalence of infection in high travel areas was expected to be 5% higher than what was observed. The observed decrease in prevalence can be directly attributed to the lack of imported cases, highlighting the importance of control measures that target these types of infections.\u003c/p\u003e","manuscriptTitle":"Impact of a six-month COVID-19 travel moratorium on Plasmodium falciparum malaria prevalence on Bioko Island, Equatorial Guinea","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-08 18:57:42","doi":"10.21203/rs.3.rs-4189942/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":"17c4f7bb-c9cc-4820-a38b-50680a94aab8","owner":[],"postedDate":"April 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":30307025,"name":"Health sciences/Risk factors"},{"id":30307026,"name":"Health sciences/Diseases/Infectious diseases/Malaria"}],"tags":[],"updatedAt":"2024-09-28T07:07:02+00:00","versionOfRecord":{"articleIdentity":"rs-4189942","link":"https://doi.org/10.1038/s41467-024-52638-2","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2024-09-27 04:00:00","publishedOnDateReadable":"September 27th, 2024"},"versionCreatedAt":"2024-04-08 18:57:42","video":"","vorDoi":"10.1038/s41467-024-52638-2","vorDoiUrl":"https://doi.org/10.1038/s41467-024-52638-2","workflowStages":[]},"version":"v1","identity":"rs-4189942","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4189942","identity":"rs-4189942","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}