A comprehensive analysis of non-pharmaceutical interventions and vaccination on Ebolavirus disease outbreak: Stochastic modeling approach

Ebolavirus disease (EVD) outbreaks have intermittently occurred since the first documented case in the 1970s. Due to its transmission characteristics, large outbreaks have not been observed outside Africa. However, within the continent, significant outbreaks have been attributed to factors such as endemic diseases with similar symptoms and inadequate medical infrastructure, which complicate timely diagnosis. In this study, we employed a stochastic modeling approach to analyze the spread of EVD during the early stages of an outbreak, with an emphasis on inherent risks. We developed a model that considers medical staff and unreported cases, and assessed the effect of non-pharmaceutical interventions (NPIs) using actual data. Our results indicate that the implementation of NPIs led to a decrease in the transmission rate and infectious period by 30% and 40% respectively, following the declaration of the outbreak. We also investigated the risks associated with delayed outbreak recognition. Our simulations suggest that, when accounting for NPIs and recognition delays, prompt detection could have resulted in a similar outbreak scale, with approximately 50% of the baseline NPIs effect. Finally, we discussed the potential effects of a vaccination strategy as a follow-up measure after the outbreak declaration. Our findings suggest that a vaccination strategy can reduce both the burden of NPIs and the scale of the outbreak. Author summary Our research employs a stochastic model to analyze the early-stage spread of Ebolavirus Disease. We incorporated factors such as medical staffs and unreported cases, and utilized real data to evaluate the impact of non-pharmaceutical interventions on disease transmission. Our findings indicate that rapid outbreak recognition could effectively control disease spread with reduced efforts. Furthermore, we explored the potential implementation of a vaccination strategy following an outbreak declaration. Our results suggest that such a strategy could mitigate both the scale of the outbreak and the necessity for additional interventions.

1 Ebolavirus Disease (EVD) was first identified in 1976 in Sudan and the Democratic 2 Republic of Congo [1,2]. The scale and impact of EVD outbreaks have evolved over 3 time. The 2013-2016 West Africa epidemic was notably the most severe outbreak, 4 February 1, 2024 1/12 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 27, 2024. ; https://doi.org/10.1101/2024.02.25.24302269doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. resulting in over 11,000 deaths [3]. The most recent outbreak occurred in Uganda in 5 2022, with the first case identified on September 19, leading to an official outbreak 6 declaration the following day. This outbreak lasted approximately four months, with 7 164 confirmed cases and 77 deaths [4]. The management and response to EVD 8 outbreaks pose several challenges. For instance, cases may go unreported due to the 9 initial symptoms being easily mistaken for other diseases, leading to transmission to 10 medical staffs (MS) during the early stages of an outbreak [5,6]. This challenge is 11 particularly prevalent in remote areas and regions with limited medical facilities [7]. 12 As of January 2024, two vaccines have been approved: Ervebo and 13 Zabdeno/Mvabea [8]. Ervebo, a single-dose vaccine, is primarily used for emergency 14 response and has demonstrated near 100% efficacy in preventing infection immediately 15 after vaccination [9]. However, it is only effective against Zaire ebolavirus and has poor 16 storage stability, requiring use within 4 hours at room temperature and temperatures 17 below -60°C for long-term storage [10]. In contrast, Zabdeno/Mvabea, a two-dose 18 vaccine administered to healthcare workers in advance, is speculated to have a relatively 19 lesser preventive effect than Ervebo [10]. It requires an 8-week vaccination period for 20 the two doses, making it unsuitable for immediate outbreak response, but it has better 21 storage stability, remaining viable for up to a year at regular refrigerator 22 temperatures [10]. 23 The application of these vaccines during outbreaks offers further insights. During 24 the 2013-2016 West Africa outbreak, ring vaccination commenced in April 2015, a 25 period when the outbreak was subsiding. The vaccination was experimental, with a 26 small number of approximately 3,000 individuals vaccinated compared to the overall 27 scale of the outbreak, and the effectiveness of the vaccine was measured during the same 28 period [11]. In the 2018-2020 Kivu epidemic, vaccination started a week after the 29 outbreak was declared, and approximately 300,000 individuals were vaccinated in 30 total [12]. However, despite rapid recognition and the application of both 31 non-pharmaceutical interventions (NPIs) and vaccination, controlling the spread was 32 challenging due to conflict, insecurity, and misinformation [13–15]. 33 Utilizing mathematical modeling to study infectious diseases provides a systematic 34 structure that is crucial for deciphering and forecasting disease transmission 35 dynamics [16,17]. A significant advantage of such modeling is its ability to provide 36 quantitative insights. Rather than making decisions based on general observations, 37 these models utilize detailed numerical data. This precise data assists policymakers in 38 understanding the outcomes of potential interventions, including vaccination campaigns, 39 travel restrictions, and the enforcement of social distancing measures [18 –20]. Numerous 40 studies have primarily focused on the mathematical modeling of transmission dynamics 41 and control strategies related to EVD outbreaks. Previous research has investigated the 42 initial transmission patterns during the 2014 West African EVD outbreak to quantify 43 the disease’s transmissibility and the impact of NPIs [21,22]. The potential risks 44 associated with importing the pathogen into non-African countries and the inherent 45 threats of large-scale outbreaks were examined [23,24]. Several studies have utilized 46 contact tracing strategies to assess the effectiveness of various containment and 47 intervention approaches [25,26]. 48 Numerous studies have concentrated on vaccination strategies for EVD outbreaks. 49 Masterson analyzed the required level of preventive vaccines based on the basic 50 reproduction number within a population and concluded that the ideal vaccination 51 coverage is unrealistic due to the high requirement [27]. Chowell used an 52 individual-based model to evaluate the impact of vaccine strategies on outbreak control 53 and found that ring-vaccination alone would not be effective in controlling the epidemic 54 in situations where there is a delay in vaccination [28]. Wells conducted a spread 55 analysis in Congo using a spatiotemporal model and observed that the vaccine program 56 February 1, 2024 2/12 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 27, 2024. ; https://doi.org/10.1101/2024.02.25.24302269doi: medRxiv preprint reduced the risk areas by up to 70.4% and decreased the risk level within those areas by 57 up to 70.1% [29]. Lastly, Potluri found that if preventive vaccine strategies are applied 58 to healthcare workers and the general population, the scale and mortality of EVD 59 outbreaks can be significantly reduced, even considering only imperfect vaccine 60 effects [30]. 61 In this study, we investigated the early stages of the EVD outbreak, considering 62 various key factors associated with potential risks. In modeling the EVD outbreak, we 63 adopted a comprehensive approach by considering the roles of MS, unreported cases, 64 and the lag between the emergence and detection of the initial case while assessing the 65 effect of NPIs, including vaccination strategies. While some of the factors we 66 incorporated for EVD have been investigated in previous research, our methodology is 67 unique as it combines unreported cases, MS, NPIs, and vaccines into a single detailed 68 model and distinctly measures the outcomes. 69

70 Modeling of EVD outbreak 71 In the modeling of the EVD outbreak, we considered the following groups: susceptible 72 (S), exposed (before symptom onset, E), infectious (post-symptom onset, I), 73 hospitalized (Q), and recovered (R ). We further divided the infectious group into I1 74 and I2 to differentiate between reported and unreported cases. We hypothesized that 75 hospitalized patients were effectively isolated and could not transmit the disease. We 76 incorporated MS by adding groups with the subscript M, and found that there were no 77 unreported cases among the MS. Fig 1 outlines the entire progression of the disease. 78 Solid arrows indicate infection events characterized by non-delayed reactions 79 (Markovian processes). In contrast, dashed arrows denote disease progression and 80 delayed reactions, which are non-Markovian processes. 81 Fig 1. Flow diagram of the Ebolavirus disease transmission model. Medical staffs and unreported cases are considered. Solid-line arrows signify nondelayed reactions, whereas dashed-line arrows denote delayed reactions. The non-delayed reactions in infection transmission are described as follows for both 82 MS and non-MS: 83 pM βSM (I1 + I2 + IM) N , βS (I1 + I2 + IM) N , where N = S + SM + E + EM + I1 + I2 + IM + R. (1) The parameter β is the transmission rate and pM represents the heightened risk 84 factor associated with MS. The parameter pM is determined to be 254.55, ascertained 85 from the case number ratio of non-MS to MS (50:14) and the population size ratio 86 (1000:1.1) in Mubende province, where the study was conducted [31,32]. These data 87 indicated that MS poses a greater risk of infection by 254.55. We estimated the value of 88 β without NPIs at 0.19, assuming that the basic reproductive number is 2.5 and the 89 average infectious period is 5.79 days [33–35]. We set the case fatality rate f to 90 0.44 [36]. The subsequent subsection discusses the report rate, represented by ρ, which 91 varies based on the outbreak detection. 92 We used modified Gillespie algorithm to simulate our model and ran simulation 93 10,000 runs per scenario [37]. Table 1 offers a comprehensive breakdown of the 94 propensities associated with non-delayed events and the particulars of delayed events. 95 February 1, 2024 3/12 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 27, 2024. ; https://doi.org/10.1101/2024.02.25.24302269doi: medRxiv preprint We assumed a uniform distribution of delays, except for the incubation period, owing to 96 a lack of data. 97 T able 1. Characteristics of the propensity of nondelayed events and details of delayed events. Event Type Description Reference Infection of MS Nondelayed Propensity: pM βSM I1+I2+IM N [33–35] Infection of non-MS Nondelayed Propensity: βS I1+I2+IM N [31–35] From exposure to onset Delayed Log-normal distribution, Mean: 9, SD: 4.31 [38] Symptom onset to hospitalization (non-MS) Delayed Uniform distribution, 5.79 ± 3.30 [34,35] Symptom onset to hospitalization (MS) Delayed Uniform distribution, 0.5 ± 0.5 Assumed From hospitalization to recovery Delayed Uniform distribution, 20.38 ± 7.58 [34,35] From hospitalization to death Delayed Uniform distribution, 5.56 ± 6.11* [34,35] *If the generated value is lesser than 0, then the value changes to 0. In real, this is the case when the patient dies before the hospitalization. Scenarios for model simulation 98 For a baseline scenario, we focused on the outbreak within the Mubende district, the 99 epicenter of the 2022 Ugandan EVD outbreak. The simulation encompassed two stages, 100 accounting for behavioral alterations and NPIs after the outbreak announcement: the 101 phase before the declaration (P 1) and after the declaration (P 2). Fig 2 graphically 102 describes and clarifies this phase division. A primary case refers to an individual 103 introducing the infection into a population, whereas an index case denotes the first 104 identified case [39]. The primary case can be the index case, but not necessarily. 105 To set the effect of NPIs for the baseline scenario, we assumed that the transmission 106 rate and duration from symptom onset to hospitalization decreased by 30% and 40%, 107 respectively, upon outbreak declaration. Note that these coupled values (30% and 40%) 108 are chosen to simulate real incidence and described in subsequent section. The criterion 109 for this declaration was 19 days after the first death, which is within the reported group 110 and consistent with the situation in Uganda. In 2022, it was ascertained in Mubende 111 district that six deaths, later confirmed, had occurred before the official outbreak 112 declaration [40,41]. Investigations indicated the potential for 17 more probable deaths 113 before this declaration [42]. Based on these data, the reporting rate in the 114 pre-declaration phase was estimated to be 7/24 (29.17%). In the post-declaration phase, 115 with 22 confirmed deaths and two probable deaths, the estimated rate was 22/24 116 (91.67%). The report rate also shifted (from P 1 to P 2) when the outbreak was declared. 117 Furthermore, at the outbreak declaration, we assumed that previously unreported 118 individuals are later reported based on the difference between the two reporting rates. 119 Fig 2. Division of phases considering outbreak declaration and setting for baseline model simulation scenario. To consider comparable scenarios, we explored the effects of varying the thresholds 120 for outbreak declaration, the intensity of NPIs on the spread of the disease, and 121 February 1, 2024 4/12 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 27, 2024. ; https://doi.org/10.1101/2024.02.25.24302269doi: medRxiv preprint vaccination. The key variations considered are as follows: 122 • Threshold for outbreak declaration: The delay from the first death to outbreak 123 declaration varied from 1 to 38 days. 124 • Effect of NPIs: The effect of NPIs on the transmission rate and infectious period 125 varied. This variation ranged from a 50% reduction (more stringent NPIs) to an 126 increase of 50% (less severe NPIs) relative to the baseline setting. 127 • Vaccination: It is assumed that a person is immediately immune, i.e., hosts in 128 state S transfer to R, once the vaccination is completed within a certain period 129 (minimum 10 days, maximum 90 days) after the outbreak is declared. Proportion 130 of vaccinated individuals is ranged from 0.1 to 0.3, whereas all of MS are 131 vaccinated. 132

133 Baseline scenario simulation 134 Fig 3 shows the baseline simulation results for cumulative confirmed cases. The gray 135 curves depict the outcomes of each distinct simulation run, the dark curve signifies the 136 mean, and the red boxes show the trends of confirmed cases in Mubende district. 137 Because of inherent randomness, the timing of the outbreak declaration differs across 138 runs; therefore, all simulation outcomes were synchronized based on the timing of the 139 outbreak declaration. The actual number of confirmed cases in Mubende District was 140 66. The simulation mean value was 66.84, with a 95% credible interval (CrI) ranging 141 from 0 to 226. 142 Fig 3. Cumulative confirmed cases from the baseline model simulation. The grey curves represent individual simulation runs, the dark curve denotes the simulation mean, and the red boxes display actual data from the Mubende district. Note that the vertical line, marking time 0, signifies the timing of the outbreak declaration in the simulation runs. In the baseline scenario simulation, the transmission rate and duration from 143 symptom onset to hospitalization (infection period) were reduced by 30% and 40%, 144 respectively, following the outbreak declaration. Thus, the real-world effect of NPIs 145 closely mirrors these levels. Nevertheless, the decline in the transmission rate might 146 have been more pronounced, whereas the reduction in the duration from symptom onset 147 to hospitalization might have been less significant or the inverse. Fig 4 shows the 148 contour lines for pairs of values with an average closely aligned with the actual data, 149 spanning a range of the effect of NPIs. Red asterisk indicates values for the baseline 150 scenario (40% and 30% of reduction of infectious period and transmissibility, 151 respectively). When comparing the X- and Y-axis intercepts, scenarios with no 152 reduction in the infectious period but a 70% reduction in the transmission rate and 153 those with no decrease in the transmission rate but a 53% reduction in the infectious 154 period showed similar simulation results. 155 Fig 4. Effect of NPIs on the simulation results for confirmed cases. The dashed cyan curve represents the contour line with an average value equivalent to the baseline scenario simulation outcome. Fig 5A presents the distribution of duration from primary case to outbreak 156 declaration (P 1), which reveals a bimodal pattern. Outbreaks are typically declared 157 February 1, 2024 5/12 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 27, 2024. ; https://doi.org/10.1101/2024.02.25.24302269doi: medRxiv preprint within seven days, with another cluster emerging at approximately 50 days. This 158 distinct pattern arises in some simulation scenarios where subsequent infections do not 159 manifest, leading to the premature end of the outbreak. The probability of an outbreak 160 concluding prematurely within one week was 19%. On average, excluding instances 161 where the disease ended early, the primary case manifested approximately 50 days prior, 162 with a 95% CrI ranging from 32 to 82 days. Fig 5B shows the distribution of P 2 163 duration and exhibits a monomodal distribution, with a mean of 64 days and a 95% CrI 164 spanning from 19 to 152 days. In this study, we defined the duration of P 2 as the 165 period from outbreak declaration to when there were no individuals in stages E or I. 166 Fig 5. Histogram representing the durations of phases. Duration from the occurrence of the primary case to the outbreak declaration (A), and from the outbreak declaration to the end of outbreak (B). Addressing how many individuals were infected when the outbreak declaration was 167 officially acknowledged is essential for planning and responding. Fig 6 illustrates the 168 distribution of prevalence by status at the outbreak declaration. Mean number (95% 169 CrI) of E, EM, I1, I2, and IM are 12.07, 3.04, 1.64, 7.34, and 0.16 ([0,45], [0,11], [0,7], 170 [0,27], and [0,1]), respectively. When normalized by population size, the number of 171 exposed MS is 27.62 per 1,000. This ratio is 229 times higher than the non-MS group, 172 which registers at 0.12 per 1,000. 173 Fig 6. Distribution of prevalence by each status at the time of outbreak declaration. Scenarios considering NPIs and outbreak detection 174 We examined the distribution of confirmed case numbers across various settings, 175 ranging from 1 to 38 days leading up to the outbreak declaration from the occurrence of 176 the first death (or variations in NPIs levels ranging from -50% to +50% relative to the 177 baseline). Fig 7A (Fig 7B) shows the delay range (NPIs levels) on the x-axis against the 178 number of confirmed cases on the y-axis. As expected, with an increase in the delay, the 179 number of infections also increased, exhibiting an exponential rather than a linear 180 growth pattern. Within the 95% CrI, the maximum outbreak size surged from 111 181 individuals when declared a day after the first death to 523 after a 38-day delay. On the 182 other hand, the number of cases decreases as NPIs level increases, 43 in the minimum 183 ([0,161] 95% CrI) once NPIs level is maximized. When the NPIs level is set as minimum 184 (-50%), the mean number of cases reaches 177 ([0, 585] 95% CrI). 185 Fig 7. Mean and 95% CrI of confirmed cases considering different factors: Periods leading to outbreak declaration (A), relative intensity of NPIs (B) Here, we present the outcomes of simulations that concurrently adjust for the 186 previously discussed factors: the timing of outbreak recognition and the intensity of 187 NPIs. Fig 8 maps the NPI intensity on the x-axis against the duration from the first 188 death occurrence to the outbreak declaration on the y-axis. The mean number of cases 189 in each simulation setting is depicted using a color map. For comparison with the 190 baseline scenario outcomes, we integrated contour curves corresponding to the average 191 number of infections in the baseline scenario (yellow, 67) and half (green) and double 192 (red) that count into the graph. 193 After examining the baseline contour contour, if an outbreak is declared merely a 194 day after the first death, the intensity of the NPIs can be diminished by 45% to attain a 195 February 1, 2024 6/12 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 27, 2024. ; https://doi.org/10.1101/2024.02.25.24302269doi: medRxiv preprint similar outbreak magnitude. By contrast, if the outbreak declaration occurs 26 days 196 after the first death, the NPIs must be augmented by 50% to match the baseline 197 outbreak scale. Given a constant NPIs level, outbreak recognition must be advanced by 198 approximately two weeks to cut the infection count by half. Conversely, even with 199 increased NPIs at the baseline recognition juncture, halving the infection scale was 200 impossible. 201 Fig 8. Outbreak scale determined by the intensity of NPIs and the timing of outbreak recognition. Dashed curves represent contours: yellow denotes the outbreak scale from the baseline, whereas green and red indicate half and double the size of the baseline simulation, respectively. Let us examine the effect of the vaccination strategy. Fig 9 depicts the mean number 202 of confirmed cases in relation to the timing of when the vaccination is completed. The 203 color of the curves (blue, red, and yellow) represents the proportion of the population 204 that has been vaccinated (10, 20, and 30%). As the vaccination process is expedited or 205 a larger proportion of the population is vaccinated, the number of cases decreases. 206 Conversely, if the vaccination is delayed, the number of cases converges to the number 207 in the baseline scenario (approximately 67). Table 2 lists simulation results. Note that 208 the duration from primary case occurrence to outbreak declaration ( P 1) was not 209 considered in this table, because NPIs and vaccines are post-outbreak measures. 210 Fig 9. Mean number of confirmed cases considering vaccination strategy . X- and y- axis indicate the duration from outbreak declaration to the vaccination finalizing time and mean number of confirmed cases, respectively. T able 2. Simulation results considering different vaccination rate and duration after outbreak declaration. Vaccination rate (%) Duration (day) Confirmed cases P 2 duration (day) Mean 95% CrI Mean 95% CrI 10 20 42 [ 1, 157 ] 99 [ 15, 194 ] 40 47 [ 1, 154 ] 99 [ 15, 182 ] 60 63 [ 1, 214 ] 105 [ 15, 185 ] 80 67 [ 1, 236 ] 108 [ 15, 191 ] 20 20 29 [ 1, 108 ] 89 [ 14, 178 ] 40 40 [ 1, 123 ] 94 [ 14, 170 ] 60 59 [ 1, 193 ] 103 [ 15, 176 ] 80 67 [ 1, 231 ] 107 [ 14, 189 ] 30 20 19 [ 0, 69 ] 75 [ 7, 159 ] 40 32 [ 0, 101 ] 82 [ 7, 151 ] 60 53 [ 0, 190 ] 93 [ 7, 165 ] 80 61 [ 0, 225 ] 98 [ 7, 183 ] Similar to what Fig 8 represents, Fig 10 displays mean number of confirmed cases 211 considering vaccination timing and the intensity of NPIs simultaneously. Fig 10A to C 212 contain different simulation results considering various vaccinated proportion of 213 individuals. Fig 10D displays contour curves aggregated from results in Fig 10A to C. 214 Solid (dashed) curves indicate the mean (half mean) number of confirmed cases from 215 the baseline scenario. If the vaccination proportion is set to be 20% and is finalized 50 216 days after the outbreak declaration, the intensity of NPIs that result in the same 217 number of infections as the baseline scenario was reduced by 40%. Intersection of the 218 February 1, 2024 7/12 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 27, 2024. ; https://doi.org/10.1101/2024.02.25.24302269doi: medRxiv preprint blue solid curve and yellow dashed curve indicates the scenario where the confirmed 219 cases could be reduced by half if vaccines can be administered three folds in the same 35 220 days with 40% eased NPIs. 221 Fig 10. Impact of NPIs and vaccination application. Mean number of confirmed cases where 10%, 20%, 30% of individuals are vaccinated (A-C), contour lines representing the mean number of confirmed cases occurred in the baseline scenario (solid) and half of it (dashed) where the vertical dashed grey line indicates the baseline NPIs intensity (D).

222 Our model structure, which distinguishes between MS and non-MS as well as reported 223 and unreported cases, provides a detailed understanding of the transmission dynamics. 224 A simple observation of the data reveals a high risk of MS exposure, given the 225 proportion of MS to the total population and the number of infected individuals. The 226 simulation results of our model highlight the high uncertainty of an outbreak, as 95% 227 CrI ranging from 0 to 226 was observed, emphasizing the importance of promptly 228 identifying infected MS once the index case is diagnosed. This underscores the need for 229 enhanced protective measures and training [6]. As Fig 6 indicates, numerous MS could 230 be exposed to the disease, necessitating early and aggressive interventions to identify 231 cases targeting MS. 232 The simulation of the baseline scenario suggests that the real-world impact of NPIs 233 closely reflects the reduction in the transmission rate and the duration from symptom 234 onset to hospitalization. Furthermore, our simulation results proposed a variety of NPIs 235 that could have been implemented in real-world scenarios. For example, we estimated 236 the effects of NPIs on the transmission rate and infectious period to be 30% and 40%, 237 respectively. However, as Fig 4 demonstrates, these could have been a combination of 238 different values. Our simulation results show the potential to decrease the scale of an 239 outbreak by shortening the infectious period (or reducing the transmission rate), 240 pushing the number towards the upper contours. 241 The patterns observed in past EVD outbreaks are evident: late detection, 242 inadequate intervention, misinformation, and larger, interconnected populations 243 exacerbate the situation. The West Africa and Kivu epidemics, two significant EVD 244 outbreaks, were the result of these factors. Our model simulation, which did not 245 account for nationwide populations, could not predict an epidemic of that magnitude. 246 However, our simulation still demonstrated exponential growth in the number of 247 confirmed cases as detection was delayed (Fig 7A). On the other hand, in regions with 248 low inter-regional connectivity and population density, small-scale outbreaks could occur 249 even with misdiagnosis/diagnostic delays, as evidenced by the Gabon outbreak in 250 1994 [43]. In essence, efforts for early detection of EVD spread should not be uniformly 251 distributed across all areas. Instead, if surveillance capacity is strategically focused on 252 areas where the disease is likely to spread, significant effects could be observed. 253 Our scenario-based study, which varied the timing of the outbreak declaration and 254 the intensity of NPIs (Fig 8), provides valuable insights for policymakers. The results 255 suggest that early recognition and declaration of an outbreak can significantly mitigate 256 the intensity of NPIs required to control the outbreak similarly. In contrast, delays in 257 outbreak recognition necessitate more aggressive NPIs to control outbreak. Localized 258 interventions aimed at identifying confirmed cases among patients with EVD-like 259 symptoms are less burdensome in terms of cost, effort, and manpower requirements 260 than regional lockdowns and nationwide interventions. These findings underscore the 261 importance of early detection. 262 February 1, 2024 8/12 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 27, 2024. ; https://doi.org/10.1101/2024.02.25.24302269doi: medRxiv preprint Vaccine intervention has been observed to significantly reduce the outbreak size, 263 duration, and the burden of NPIs. However, given the storage characteristics of vaccines 264 and the state of medical infrastructure, it is inevitable that the introduction of vaccines 265 will take time. This paradoxically emphasizes the importance of NPIs (Fig 10). 266 Moreover, even with rapid vaccination, there may be limitations to the vaccine supply. 267 As observed during the Kivu epidemic, when rapid vaccination was implemented, NPIs 268 remained necessary and effective measures. The simulation was conducted based on the 269 Everbo vaccine, which is highly effective with a single dose. However, the Everbo 270 vaccine is effective against the Zaire Ebolavirus, and the case in Uganda involved the 271 Sudan Ebolavirus, not the Zaire strain. This highlights the need for vaccine 272 development, as simulations have shown that the burden of NPIs in future outbreak 273 situations would decrease if a vaccine is available. 274 This study had several limitations. Firstly, although the MS group was considered 275 separately in the population, the locations where they stay (hospitals or clinics) were 276 not distinguished. Additionally, the vaccination did not reflect the target age of the 277 vaccine. For instance, in the case of Eberbo, the target age was 17 years and older, but 278 this study did not reflect the target age and only used a certain percentage of the total 279 population [44]. Furthermore, the risk of transmission due to the EVD-transmissible 280 semen of recovered patients, found in several cases during past EVD outbreaks, was not 281 reflected [45]. These limitations will be addressed in future work. 282 Acknowledgments 283 This research was supported by the Government-wide R&D Fund Project for Infectious 284 Disease Research (GFID), Republic of Korea (grant No. HG23C1629). This paper is 285 supported by the Korea National Research Foundation (NRF) grant funded by the 286 Korean government (MEST) (NRF-2021M3E5E308120711). 287

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(which was not certified by peer review) The copyright holder for this preprint this version posted February 27, 2024. ; https://doi.org/10.1101/2024.02.25.24302269doi: medRxiv preprint . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 27, 2024. ; https://doi.org/10.1101/2024.02.25.24302269doi: medRxiv preprint . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 27, 2024. ; https://doi.org/10.1101/2024.02.25.24302269doi: medRxiv preprint . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 27, 2024. ; https://doi.org/10.1101/2024.02.25.24302269doi: medRxiv preprint . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 27, 2024. ; https://doi.org/10.1101/2024.02.25.24302269doi: medRxiv preprint . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 27, 2024. ; https://doi.org/10.1101/2024.02.25.24302269doi: medRxiv preprint . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 27, 2024. ; https://doi.org/10.1101/2024.02.25.24302269doi: medRxiv preprint . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 27, 2024. ; https://doi.org/10.1101/2024.02.25.24302269doi: medRxiv preprint