Adaptation of the COVASIM model to incorporate non-pharmaceutical interventions: Application to the Dominican Republic during the second wave of COVID-19
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Alimohamadi, Y., Sepandi, M., Taghdir, M., & Hosamirudsari, H. (2020). Determine the most common clinical symptoms in COVID-19 patients: a systematic review and meta-analysis. J. Prev. Med. Hyg., 61(3), E304.Search in Google Scholar
Sáez, C., Romero, N., Conejero, J. A., & García-Gómez, J. M. (2021). Potential limitations in COVID-19 machine learning due to data source variability: A case study in the ncov2019 dataset. J. Am. Med. Inform. Assoc., 28(2), 360–364.Search in Google Scholar
Zhou, L., Romero-García, N., Martínez-Miranda, J., Conejero, J. A., et al. (2022). Subphenotyping of Mexican patients with COVID-19 at preadmission to anticipate severity stratification: age-sex unbiased meta-clustering technique. JMIR Public Health Surveill., 8(3), e30032.Search in Google Scholar
Beigel, J. H., Tomashek, K. M., Dodd, L. E., Mehta, A. K., et al. (2020). Remdesivir for the treatment of COVID-19 preliminary report. N. Engl. J. Med., 383(19), 1813–1836.Search in Google Scholar
Felsenstein, S., Herbert, J. A., McNamara, P. S., & Hedrich, C. M. (2020). COVID-19: Immunology and treatment options. Clin. Immunol., 215, 108448.Search in Google Scholar
Gysia, D. M., do Vallea, I., Zitnikd, M., Amelib, A., et al. (2021). Network medicine framework for identifying drug repurposing opportunities for COVID-19. Proc. Natl. Acad. Sci. U.S.A., 118(19), e2025581118.Search in Google Scholar
Cunningham, A. C., Goh, H. P., & Koh, D. (2020). Treatment of COVID-19: old tricks for new challenges. Crit. Care, 24, 1–2.Search in Google Scholar
Bertozzi, A. L., Franco, E., Mohler, G., Short, M. B., & Sledge, D. (2020). The challenges of modeling and forecasting the spread of COVID-19. Proc. Natl. Acad. Sci. U.S.A., 117(29), 16732–16738.Search in Google Scholar
Estrada, E. (2020). COVID-19 and SARS-CoV-2. Modeling the present, looking at the future. Phys. Rep., 869, 1–51.Search in Google Scholar
Arenas, A., Cota, W., Gómez-Gardeñes, J., Gómez, S., et al. (2020). Modeling the spatiotemporal epidemic spreading of COVID-19 and the impact of mobility and social distancing interventions. Phys. Rev. X, 10(4), 041055.Search in Google Scholar
Muñoz-Fernández, G. A., Seoane, J. M., & Seoane-Sepúlveda, J. B. (2021). A SIR-type model describing the successive waves of COVID-19. Chaos, Solitons & Fractals, 144, 110682.Search in Google Scholar
Angeli, M., Neofotistos, G., Mattheakis, M., & Kaxiras, E. (2022). Modeling the effect of the vaccination campaign on the COVID-19 pandemic. Chaos, Solitons & Fractals, 154, 111621.Search in Google Scholar
Kumar, P., Erturk, V. S., & Murillo-Arcila, M. (2021). A new fractional mathematical modelling of COVID-19 with the availability of vaccine. Results Phys., 24, 104213.Search in Google Scholar
Liu, K., & Lou, Y. (2022). Optimizing COVID-19 vaccination programs during vaccine shortages: A review of mathematical models. Infect. Dis. Model.Search in Google Scholar
Lozano, M. A., Garibo-i Orts, Ò., Piñol, E., Rebollo, M., et al. (2021). Open data science to fight COVID-19: Winning the 500k XPRIZE Pandemic Response Challenge. In Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track: European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part IV (pp. 384–399). Springer.Search in Google Scholar
Lozano, M. A., Garibo-i Orts, Ò., Piñol, E., Rebollo, M., et al. (2022). Open data science to fight COVID-19: Winning the 500k XPRIZE Pandemic Response Challenge (extended abstract). In L. De Raedt (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22 (pp. 5304–5308). International Joint Conferences on Artificial Intelligence Organization.Search in Google Scholar
Janko, V., Resčič, N., Vodopija, A., Susič, D., et al. (2023). Optimizing non-pharmaceutical intervention strategies against COVID-19 using artificial intelligence. Front. Pub. Health, 11.Search in Google Scholar
Chao, D., Halloran, M., Obenchain, V., & Longini Jr, I. (2010). FluTE, a publicly available stochastic influenza epidemic simulation model. PLoS Comput. Biol., 6(1), e1000656.Search in Google Scholar
Koo, J., Cook, A., Park, M., Sun, Y., et al. (2020). Interventions to mitigate early spread of SARSCOV-2 in Singapore: a modelling study. Lancet Infect. Dis., 20(6), 678–688.Search in Google Scholar
Moreno López, J., Arregui García, B., Bentkowski, P., Bioglio, L., et al. (2021). Anatomy of digital contact tracing: Role of age, transmission setting, adoption, and case detection. Sci. Adv., 7(15), eabd8750.Search in Google Scholar
Faucher, B., Assab, R., Roux, J., Levy-Bruhl, D., et al. (2022). Agent-based modelling of reactive vaccination of workplaces and schools against COVID-19. Nat. Commun., 13(1), 1–11.Search in Google Scholar
Rockett, R. J., Arnott, A., Lam, C., Sadsad, R., et al. (2020). Revealing COVID-19 transmission in Australia by SARSCOV-2 genome sequencing and agent-based modeling. Nature Med., 26(9), 139Search in Google Scholar
Howick, S., McLafferty, D., Anderson, G. H., Pravinkumar, S. J., et al. (2021). Evaluating intervention strategies in controlling coronavirus disease 2019 (COVID-19) spread in care homes: An agent-based model. Infect. Control & Hosp. Epidemiol., 42(9), 1060–1070.Search in Google Scholar
Truszkowska, A., Behring, B., Hasanyan, J., Zino, L., et al. (2021). High-resolution agent-based modeling of COVID-19 spreading in a small town. Adv. Theory Simul., 4(3), 2000277.Search in Google Scholar
Yin, L., Zhang, H., Li, Y., Liu, K., et al. (2021). A data-driven agent-based model that recommends nonpharmaceutical interventions to suppress coronavirus disease 2019 resurgence in megacities. J. R. Soc. Interface, 18(181), 20210112.Search in Google Scholar
Hoertel, N., Blachier, M., Blanco, C., Olfson, M., et al. (2020). A stochastic agent-based model of the SARS-CoV-2 epidemic in France. Nature Med., 26(9), 1417–1421.Search in Google Scholar
Romero-Brufau, S., Chopra, A., Ryu, A. J., Gel, E., et al. (2021). Public health impact of delaying second dose of BNT162b2 or mRNA-1273 COVID-19 vaccine: simulation agent-based modeling study. BMJ, 373.Search in Google Scholar
Hinch, R., Probert, W. J. M., Nurtay, A., Kendall, M., et al. (2021). OpenABM - Covid19 - An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing. PLoS Comput. Biol., 17(7), e1009146.Search in Google Scholar
Kano, T., Yasui, K., Mikami, T., Asally, M., & Ishiguro, A. (2021). An agent-based model of the interrelation between the COVID-19 outbreak and economic activities. Proc. Math. Phys. Eng. Sci., 477(2245), 20200604.Search in Google Scholar
Silva, P. C. L., Batista, P. V. C., Lima, H. S., Alves, M. A., et al. (2020). COVID-ABS: An agent-based model of COVID19 epidemic to simulate health and economic effects of social distancing interventions. Chaos, Solitons & Fractals, 139, 110088.Search in Google Scholar
Rahman, T., Taghikhah, F., Paul, S. K., Shukla, N., & Agarwal, R. (2021). An agent-based model for supply chain recovery in the wake of the COVID-19 pandemic. Comput. Ind. Eng., 158, 107401.Search in Google Scholar
Ferguson, N., Laydon, D., Nedjati Gilani, G., Imai, N., Ainslie, K., et al. (2020). Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand.Search in Google Scholar
Chao, D. L., Oron, A. P., Srikrishna, D., & Famulare, M. (2020). Modeling layered non-pharmaceutical interventions against SARS-CoV-2 in the United States with Corvid. MedRxiv, 2020–04.Search in Google Scholar
Krivorotko, O., Sosnovskaia, M., Vashchenko, I., Kerr, C., & Lesnic, D. (2022). Agent-based modeling of COVID-19 outbreaks for New York state and UK: Parameter identification algorithm. Infect. Dis. Model., 7(1), 30–44.Search in Google Scholar
Kerr, C., Stuart, R., Mistry, D., Abeysuriya, R., et al. (2021). Covasim: an agent-based model of COVID-19 dynamics and interventions. PLoS Comput. Biol., 17(7), e1009149.Search in Google Scholar
Hale, T., Angrist, N., Goldszmidt, R., Kira, B., et al. (2021). A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nat. Hum. Behav., 5(4), 529–538.Search in Google Scholar
Shen, Y., Powell, G., Ganser, I., Zheng, Q., et al. (2021). Monitoring non-pharmaceutical public health interventions during the COVID-19 pandemic. Sci. Data, 8(1), 225.Search in Google Scholar
Zheng, Q., Jones, F. K., Leavitt, S. V., Ung, L., et al. (2020). HIT-COVID, a global database tracking public health interventions to COVID-19. Sci. Data, 7(1), 286.Search in Google Scholar
Porcher, S. (2020). Response2covid19, a dataset of governments’ responses to COVID-19 all around the world. Sci. Data, 7(1), 423.Search in Google Scholar
Cheng, C., Barceló, J., Hartnett, A. S., Kubinec, R., & Messerschmidt, L. (2020). COVID-19 government response event dataset (Coronanet v. 1.0). Nature Human Behav., 4(7), 756–768.Search in Google Scholar
Elgin, C., Basbug, G., & Yalaman, A. (2020). Economic policy responses to a pandemic: Developing the COVID-19 economic stimulus index. Covid Economics, 1(3), 40–53.Search in Google Scholar
Davies, N. G., Abbott, S., Barnard, R. C., Jarvis, C. I., Kucharski, A. J., et al. (2021). Estimated transmissibility and impact of SARS-CoV-2 lineage b.1.1.7 in England. Science, 372(6538), eabg3055.Search in Google Scholar
Miikkulainen, R., Francon, O., Meyerson, E., Qiu, X., Sargent, D., Canzani, E., & Hodjat, B. (2021). From prediction to prescription: Evolutionary optimization of nonpharmaceutical interventions in the COVID-19 pandemic. IEEE Trans. Evol. Comput, 25(2), 386–401.Search in Google Scholar
Hale, T., Angrist, N., Goldszmidt, R., Kira, B., et al. (2021). COVID-19 Government Response Tracker. https://github.com/OxCGRT/covid-policy-tracker/blob/master/documentation/codebook.mdSearch in Google Scholar
Valencia IA4COVID. (2021). XPRIZE Pandemic Response hallenge. https://github.com/malozano/valencia-ia4covid-xprizeSearch in Google Scholar
United Nations. (2019). Population division. https://www.un.org/development/desa/pd/Search in Google Scholar
Oliver, N., Lepri, B., Sterly, H., Lambiotte, R., et al. (2020). Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. Science Adv., 6(23), eabc0764.Search in Google Scholar
Hamer, D. H., White, L. F., Jenkins, H. E., Gill, C. J., et al. (2021). Control of COVID-19 transmission on an urban university campus during a second wave of the pandemic. medRxiv, pages 2021–02.Search in Google Scholar
Kerr, C. C., Mistry, D., Stuart, R. M., Rosenfeld, K., et al. (2021). Controlling COVID-19 via test-trace-quarantine. Nature Comm., 12(1), 2993.Search in Google Scholar