key: cord-0799488-2veg8m6h authors: Almajose, A. P.; White, A.; Diego, C.; Lazaro, R.; Austriaco, N. title: A 2SIR-VD Model for Optimizing Geographical COVID-19 Vaccine Distribution in the Philippines date: 2021-05-21 journal: nan DOI: 10.1101/2021.05.20.21257556 sha: 4fda4981b70d7864dc6e7712f122243ba01787a4 doc_id: 799488 cord_uid: 2veg8m6h COVID-19 is a novel respiratory disease first identified in Wuhan, China, that is caused by the novel coronavirus, SARS-CoV-2. It has triggered a global pandemic of historic proportions. The government of the Philippines began its national vaccine drive on March 1, 2021, with the goal of vaccinating seventy million of its citizens by the end of the calendar year. To determine the optimum geographical distribution strategy in the Philippines for the limited supply of vaccines that is currently available, we developed and adapted a basic SIR model that allows us to understand the evolution of a pandemic when public health authorities are vaccinating two susceptible populations within a country with different vaccine rates. Our analysis with our 2SIR-VD model revealed that prioritizing vaccine deployment to the National Capital Region (NCR) of the Philippines minimized the number of COVID-19 cases in the country. We therefore recommend deploying 90% of the available vaccine supply to the NCR to mitigate viral transmission there. The remaining 10% would allow the rest of the archipelago to vaccinate all of their senior citizens, thus shielding this vulnerable population against severe disease and death from COVID-19. Philippines, with all of its limitations and shortcomings. 82 Using our 2SIR-VD model, we compared five geographical distribution strategies 83 to determine which one would most efficiently end the pandemic in the Philippines: 1) 84 equal distribution throughout the country; 2) priority distribution to the NCR; 3) priority 85 distribution to the "NCR Plus" bubble established by the Philippine government during 86 the 1Q of 2021; 4) priority distribution to the three major metropolitan areas of Metro 87 Manila, Metro Cebu, and Metro Davao; and 5) priority distribution to the NCR Plus, 88 Metro Cebu, and Metro Davao. Our analysis revealed that prioritizing vaccine deployment 89 to the NCR minimized the number of COVID-19 cases in the country. We therefore 90 recommend first deploying 90% of the available vaccine supply to the NCR to mitigate 91 . CC-BY-NC-ND 4.0 International license It 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) This SIR-VD model will allow us to understand how a pandemic evolves as a vaccination 144 drive advances in a particular population. 145 We have also adapted our SIR-VD model to understand the evolution of a 146 pandemic when public health authorities are vaccinating two susceptible populations with 147 different vaccine rates. For simplicity's sake, we assume that the infected individuals that 148 belong to the distinct populations are indistinguishable, that is, there is an ideal mixing 149 between the two infected populations. Diagram (2) graphically represents our two-150 population 2SIR-VD model with the underlying assumption that the two infected 151 populations resulting from the susceptible individuals ideally mix. 152 CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 21, 2021. ; https://doi.org/10.1101/2021.05.20.21257556 doi: medRxiv preprint (2) 154 The model is mathematically represented by equations (12) . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 21, 2021. ; https://doi.org/10.1101/2021.05.20.21257556 doi: medRxiv preprint Assuming that vaccination is limited to a certain amount , the following 165 equation must hold true: 166 An important ramification of (18) is that the vaccinations may be weighted among the 168 two susceptible populations. This allows us to study the progress of the epidemic as two 169 populations are vaccinated with different rates. In this study, will be designated as the which are normally less than the remainder of the total susceptible individuals. Should 176 evaluate to zero, the vaccination rate will be transferred to the remainder of the total 177 susceptible individuals , that is: 178 CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (ncovtracker.doh.gov.ph). The data used for this paper was preprocessed by evaluating 186 the number of active cases, the cumulative recoveries, and cumulative deaths due to the 187 epidemic per week as provided by the tracker. We chose to include epidemic information 188 on a weekly basis to reduce the amount of data scatter and errors that we had observed 189 because of delayed reporting of information by the DOH. Further, we discovered that this 190 allowed us to properly account for the weekly recovery rates announced by the DOH. Table 1 . 199 In order to determine the rate for the systems of differential equations described CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. This NCR first scenario (91% decrease in total cases) is better than either a Metro Manila, 279 Metro Cebu and Metro Davao first scenario (87% decrease) or an NCR Plus first scenario 280 (81% decrease). Therefore, our simulation suggests that the national vaccine strategy 281 should target herd immunity in the NCR before inoculating the rest of the country in 282 order to most efficiently halt the pandemic in the Philippines. 283 Finally, we wanted to explore the effect of varying the ratio of vaccines deployed 284 to the NCR to vaccines sent to the rest of the Philippines. Numbers from the National 285 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 21, 2021. ; https://doi.org/10.1101/2021.05.20.21257556 doi: medRxiv preprint Task Force for COVID-19 from May 11, 2021, revealed that the current vaccine 286 deployment strategy of the national government distributes 40% of the national vaccine 287 supply to the NCR (2,495,970 doses to the NCR out of 6,408,640 total doses distributed). 288 As shown in Figure 5 , our simulations reveal that increasing the percentage of 289 vaccines deployed to the NCR from 40% to 100% would lower the number of total COVID-290 19 cases expected from the current strategy by 90%. However, we do not recommend 291 committing the entire vaccine supply to the NCR since this strategy does not acknowledge 292 that different segments of the population are at different risk for severe COVID-19 and 293 death. Rather, we propose that the national government commit 90% of the vaccine 294 supply to the NCR. The remaining 10% would be used to inoculate the medical frontliners 295 and senior citizens who are most at risk throughout the country. Senior citizens (60 years 296 old and above) make up about 9% of the total population of Filipinos according to 297 published statistics (https://www.populationpyramid.net/philippines/). Our model 298 revealed that deploying 90% rather than 100% of the vaccine supply to the NCR would 299 still decrease the predicted COVID-19 case load from the current strategy by 65%. 300 However, we would expect this strategy to decrease the total mortality rate since those 301 most at risk throughout the archipelago would still be inoculated first. 302 Our study is limited by the simplicity of our 2SIR-VD model, which does not take 303 into account the ever-changing non-pharmaceutical interventions that have been and are 304 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 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