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      Simulation and prediction of spread of COVID-19 in The Republic of Serbia by SEIRDS model of disease transmission

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          Abstract

          As a response to the pandemic caused by SARS-Cov-2 virus, on 15 March, 2020, the Republic of Serbia introduced comprehensive anti-epidemic measures to curb COVID-19. After a slowdown in the epidemic, on 6 May, 2020, the regulatory authorities decided to relax the implemented measures. However, the epidemiological situation soon worsened again. As of 7 February, 2021, a total of 406,352 cases of SARSCov-2 infection have been reported in Serbia, 4,112 deaths caused by COVID-19. In order to better understand the epidemic dynamics and predict possible outcomes, we have developed an adaptive mathematical model SEAIHRDS (S-susceptible, E-exposed, A-asymptomatic, I-infected, H-hospitalized, R-recovered, D-dead due to COVID-19 infection, S-susceptible). The model can be used to simulate various scenarios of the implemented intervention measures and calculate possible epidemic outcomes, including the necessary hospital capacities. Considering promising results regarding the development of a vaccine against COVID-19, the model is extended to simulate vaccination among different population strata. The findings from various simulation scenarios have shown that, with implementation of strict measures of contact reduction, it is possible to control COVID-19 and reduce number of deaths. The findings also show that limiting effective contacts within the most susceptible population strata merits a special attention. However, the findings also show that the disease has a potential to remain in the population for a long time, likely with a seasonal pattern. If a vaccine, with efficacy equal or higher than 65%, becomes available it could help to significantly slow down or completely stop circulation of the virus in human population.

          The effects of vaccination depend primarily on: 1. Efficacy of available vaccine(s), 2. Prioritization of the population categories for vaccination, and 3. Overall vaccination coverage of the population, assuming that the vaccine(s) develop solid immunity in vaccinated individuals. With expected basic reproduction number of R o=2.46 and vaccine efficacy of 68%, an 87% coverage would be sufficient to stop the virus circulation.

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          Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention

          Zunyou Wu, Jennifer M. McGoogan (2020)
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            Estimates of the severity of coronavirus disease 2019: a model-based analysis

            Robert Verity, Lucy C Okell, Ilaria Dorigatti … (2020)
            Summary Background In the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring and ascertainment biases. Methods We collected individual-case data for patients who died from COVID-19 in Hubei, mainland China (reported by national and provincial health commissions to Feb 8, 2020), and for cases outside of mainland China (from government or ministry of health websites and media reports for 37 countries, as well as Hong Kong and Macau, until Feb 25, 2020). These individual-case data were used to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the case fatality ratio by relating the aggregate distribution of cases to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for demography and age-based and location-based under-ascertainment. We also estimated the case fatality ratio from individual line-list data on 1334 cases identified outside of mainland China. Using data on the prevalence of PCR-confirmed cases in international residents repatriated from China, we obtained age-stratified estimates of the infection fatality ratio. Furthermore, data on age-stratified severity in a subset of 3665 cases from China were used to estimate the proportion of infected individuals who are likely to require hospitalisation. Findings Using data on 24 deaths that occurred in mainland China and 165 recoveries outside of China, we estimated the mean duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9–19·2) and to hospital discharge to be 24·7 days (22·9–28·1). In all laboratory confirmed and clinically diagnosed cases from mainland China (n=70 117), we estimated a crude case fatality ratio (adjusted for censoring) of 3·67% (95% CrI 3·56–3·80). However, after further adjusting for demography and under-ascertainment, we obtained a best estimate of the case fatality ratio in China of 1·38% (1·23–1·53), with substantially higher ratios in older age groups (0·32% [0·27–0·38] in those aged <60 years vs 6·4% [5·7–7·2] in those aged ≥60 years), up to 13·4% (11·2–15·9) in those aged 80 years or older. Estimates of case fatality ratio from international cases stratified by age were consistent with those from China (parametric estimate 1·4% [0·4–3·5] in those aged <60 years [n=360] and 4·5% [1·8–11·1] in those aged ≥60 years [n=151]). Our estimated overall infection fatality ratio for China was 0·66% (0·39–1·33), with an increasing profile with age. Similarly, estimates of the proportion of infected individuals likely to be hospitalised increased with age up to a maximum of 18·4% (11·0–7·6) in those aged 80 years or older. Interpretation These early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and show a strong age gradient in risk of death. Funding UK Medical Research Council.
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              A Contribution to the Mathematical Theory of Epidemics

              W O Kermack, A McKendrick (1927)
              Article 0 comments Cited 1424 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Microb Risk Anal
                Journal ID (iso-abbrev): Microb Risk Anal
                Title: Microbial Risk Analysis
                Publisher: Elsevier B.V.
                ISSN (Print): 2352-3522
                ISSN (Electronic): 2352-3530
                Publication date PMC-release: 11 March 2021
                Publication date (Electronic): 11 March 2021
                Electronic Location Identifier: 100161
                Affiliations
                [a ]Directorate of National Reference Laboratories, Batajnicki drum 10, 11080 Zemun, Serbia
                [b ]International Burch University, Francuske revolucije bb, Ilidza, 71210, Sarajevo, Bosnia and Herzegovina
                [c ]Veterinary Scientific Institute of Serbia, Janisa Janulisa 14, 11107, Belgrade, Serbia
                [d ]University of Pittsburgh, Department of Civil and Environmental Engineering, 3700 O'Hara Street, Pittsburgh, PA 15261, USA
                [e ]Belgrade University, Faculty of Veterinary Medicine – Department of Infectious Diseases and Epidemiology, Bulevar Oslobodenja 18, 11000 Belgrade, Serbia
                Author notes
                [* ]Corresponding author.
                Article
                Publisher Item ID: S2352-3522(21)00003-7 Publisher ID: 100161
                DOI: 10.1016/j.mran.2021.100161
                PMC ID: 7946545
                PubMed ID: 33723516
                SO-VID: df38e569-a9f0-4c56-aa00-d2ef6ff58da2
                Copyright © © 2021 Elsevier B.V. All rights reserved.
                License:

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                Date received : 2 November 2020
                Date revision received : 15 February 2021
                Date accepted : 3 March 2021
                Categories
                Subject: Article

                Keywords: covid-19,seaihrds mathematical model,prediction,vaccination
                Data availability:
                Keywords: covid-19, seaihrds mathematical model, prediction, vaccination

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