key: cord-0426467-qrqupygq authors: M.S., N.; John, D.; S.P., A.; Jammy, G. R.; Pant, R.; Choudhury, L. title: SHIVIR - An Agent-Based Model to assess the transmission of COVID-19 in India date: 2022-05-27 journal: nan DOI: 10.1101/2022.05.26.22275624 sha: c919c2bec37128e03a27f8b20d760394ff4b45de doc_id: 426467 cord_uid: qrqupygq Background: COVID-19 has tormented the global health and economy like no other event in the recent past. Researchers and policymakers have been working strenuously to end the pandemic completely. Methodology/ Principal Findings: Infectious disease dynamics could be well-explained at an individual level with established contact networks and disease models that represent the behaviour of the infection. Hence, an Agent-Based Model, SHIVIR (Susceptible, Infected, Admitted, ICU, Ventilator, Recovered, Immune) that can assess the transmission dynamics of COVID-19 and the effects of Non-Pharmaceutical Interventions (NPI) was developed. Two models were developed using to test the synthetic populations of Rangareddy, a district in Telangana state, and the state itself respectively. NPI such as lockdowns, masks, and social distancing along with the effect of post-recovery immunity were tested across scenarios. The actual and forecast curves were plotted till the unlock phase began in India. The Mean Absolute Percentage Error of scenario MD100I180 was 6.41 percent while those of 3 other scenarios were around 10 percent each. Since the model anticipated lifting of lockdowns that would increase the contact rate proportionately, the forecasts exceeded the actual estimates. Some possible reasons for the difference are discussed. Conclusions: Models like SHIVIR that employ a bottom-up Agent-Based Modelling are more suitable to investigate various aspects of infectious diseases owing to their ability to hold details of each individual in the population. Also, the scalability and reproducibility of the model allow modifications to variables, disease model, agent attributes, etc. to provide localized estimates across different places. been working in tandem to devise policies to curtail the spread of pandemic [4, 5] . These are achievable by 59 employing mathematical models that could assess multiple aspects like the transmission dynamics, effect 60 of Non-Pharmaceutical Interventions (NPI), the capacity of health infrastructure, etc. [6] [7] [8] . . CC-BY 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 27, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 7. Deceased: Agents who have failed to recover from the infection and died. They are no more a part 136 of the simulation. All the created agents are assigned "Healthy" status at the start of the simulation. Every agent can exist in 138 any of the above-mentioned states at an instant. Based on the contacts made by agents during the simulation, . CC-BY 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) The copyright holder for this preprint this version posted May 27, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 3.3. Contact Network Contact network was established henceforth based on the characteristics of agents in the population. It is a 152 factor that majorly affects the spread of infection across the population. The present model considered the 153 contacts made at home, schools, and work depending on place and age as defined by Prem et al., (2017) . This helped segregate the contacts like those in the closer circle (home) and external. For each agent in the 155 population, a list of close circle contacts was defined based on the closeness of their locations i.e., the 156 probability of two agents being in each other's closer circle is inversely proportional to the distance between 157 them, as in equation (1). The following two assumptions were made to work this out: 158 i. Two people who are farthest in the population have zero probability of meeting . CC-BY 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) The copyright holder for this preprint this version posted May 27, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 ii. The probability of people with the same Geographic Information System (GIS) coordinates to meet 160 is '1', which is certain in any scenario. where 'A' and 'B' are any two people within the population. The proportion of contacts made with external people was proportionately reduced for stricter lockdowns 164 while the ones in the closer circle were retained. Also, the number of contacts made by each person is to be 165 determined based on the location and age. For this, the results of a study by Supriya Kumar et al., (2018) 166 that determined the contact rates for close-contact infections were utilized. Supriya Kumar et al., (2018) The Timestep chosen for the study is days as it would be meaningful to present these time series estimates 181 daily and also that the contact rates and COVID-19 related variables are defined in days. A healthy . CC-BY 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 27, 2022. ; https://doi.org/10. 1101 /2022 individual who has been infected through contact on a day would not acquire a secondary infection. Duration of the existence of each individual in state increments each day to transform to the next state upon The number of contacts made by every agent daily and proportionate reduction during the lockdowns were 203 designed as explained in Section 3.3. For the Rangareddy model, different distributions were generated . CC-BY 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) . CC-BY 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 27, 2022. ; https://doi.org/10.1101/2022.05.26.22275624 doi: medRxiv preprint The stringency of lockdowns and the contacts made were during each phase are presented in Table I. Higher 229 the stringency of lockdown, lower the contact rate. Contact rate indicated in the percentage of contacts 230 made as compared to a no lockdown/ normal scenario. It is seen that the proportion of close contacts is 231 higher for more stringent lockdowns indicating higher contacts within the locality or at home. . CC-BY 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 27, 2022. . CC-BY 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 27, 2022. The model was simulated for 365 days by introducing an infected agent to the population. The code was 243 run on a High-Performance Computing server facility of Amrita Vishwa Vidyapeetham, India. The estimates provided by the model are governed by the variables that were gathered during the initial stages 245 of the pandemic. Also, the lockdown phase considered was based on the initial phases that were imposed 246 in India for 142 days [35] . The model increased the contact rates proportionately for each of the unlock 247 phases and assumed a 100 percent normalcy post 142 nd day, which marks the end of Unlock 3.0 (Table I) . The symptomatic infections in the model that indicate the diagnosed and identified positive cases were . CC-BY 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 27, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 CC-BY 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 27, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 Of all these interventions and control strategies that could help to curtail the spread of infection, the Telangana model included only lockdowns, use of masks, and social distancing with the effect of post- The study presented the framework to use ABM for studying infectious diseases using a synthetic 286 population approach. Infectious disease dynamics are well-explained on an individual level with contact 287 networks than compartmental which is the key idea behind the adoption of this approach [6, 58] . The 288 development of a suitable disease model to represent the behaviour of COVID-19 was the initial process. The disease model, SHIVIR was developed in this study to assess the effects of various NPIs and 290 transmission dynamics of COVID-19. The models were developed using AnyLogic (Rangareddy) and Python (Telangana). The former model is less complex as it involved only lockdown as NPI whilst the latter . CC-BY 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 27, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 model included the effect of masks, social distancing, and post-recovery immunity additionally. The Telangana model had a more complex contact network that mapped close circle contacts using GIS 294 coordinates. Also, in addition to age, the latter model included GIS, household ID, district code. The 295 simulation was run for 365 days across six different scenarios involving varied combinations of the NPIs. The lockdowns were imposed per the actual ones that were in place in India. The forecast and actual curves 297 matched closely during the initial lockdown phases. After the beginning of the unlock phases, the reduction 298 of lockdown stringency and increase in contact rate in the model spiked the estimates generated by the 299 model. Contrarily, the slope of the actual curve was much lesser than those of the estimates because of the 300 cumulative interventions in real-time. The code is expandable and reproducible in terms of the synthetic population being used, the attributes 302 mapped to each agent, addition/ deletion/ modification of existing states in the existing (SHIVIR) model, . CC-BY 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) The copyright holder for this preprint this version posted May 27, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 Funding 329 No funding support was received for this study. Ethical Approval The study has been conducted using publicly available data. No ethical approvals were sort for this study. based model of COVID-19 dynamics and interventions. medRxiv. 2020; 2020.05.10.20097469. . CC-BY 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) The copyright holder for this preprint this version posted May 27, 2022. ; https://doi.org/10.1101/2022.05.26.22275624 doi: medRxiv preprint control of the COVID-19 pandemic in Australia. arXiv. 2020; 1-31. doi:10.1038/s41467-020- 19393-6 . 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Data availability statement The synthetic population used, the AnyLogic model along with the input data are available at: 325 https://cloud.anylogic.com/model/7cd10c0c-f1c1-4b8f-9aac-0bf37a45379a?mode=SETTINGS and 326 https://osf.io/utmhg/?view_only=05ac26fc100645be8b1bba6557d606be. The code developed using Python is available at: https://doi.org/10.6084/m9.figshare.19121939.. CC-BY 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)The copyright holder for this preprint this version posted May 27, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 . CC-BY 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) . CC-BY 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) 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 27, 2022. ; https://doi.org/10.1101/2022.05.26.22275624 doi: medRxiv preprint https://www.newindianexpress.com/nation/2021/may/04/only-44-per-cent-of-india-is-wearing-a-. CC-BY 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 27, 2022. ; https://doi.org/10.1101 /2022 . CC-BY 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)The copyright holder for this preprint this version posted May 27, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022