COVID-19 Outbreak in Post-Soviet States: Modeling the Best and Worst Possible Scenarios

Abbay, Anara; Adambekov, Shalkar; Aljofan, Mohamad; Kashkynbayev, Ardak; Gaipov, Abduzhappar

doi:10.1101/2020.04.19.20071704

Cited by 8 publications

(9 citation statements)

References 21 publications

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“…The worldwide infection fatality rate was reported around 0.68% (0.53–0.82%) across the population [ 37 ] and overall mortality rate from COVID-19 is around 2.3% and varies among countries [ 1 ]. In Kazakhstan, the overall mortality rate reported is 1.6% as of November 21st [ 38 , 39 ]. Official statistic potentially underestimate mortality with uncaptured deaths from COVID-19 who died during post-discharge period, and people who could not get a medical attention and died at home or misdiagnosed.…”

Section: Discussionmentioning

confidence: 99%

SARS-CoV-2 PCR-positive and PCR-negative cases of pneumonia admitted to the hospital during the peak of COVID-19 pandemic: analysis of in-hospital and post-hospital mortality

et al. 2021

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Background During the spike of COVID-19 pandemic in Kazakhstan (June-2020), multiple SARS-CoV-2 PCR-test negative pneumonia cases with higher mortality were reported by media. We aimed to study the epidemiologic characteristics of hospitalized PCR-test positive and negative patients with analysis of in-hospital and post-hospital mortality. We also compare the respiratory disease characteristics between 2019 and 2020. Methods The study population consist of 17,691 (March–July-2020) and 4600 (March–July-2019) hospitalized patients with respiratory diseases (including COVID-19). The incidence rate, case-fatality rate and survival analysis for overall mortality (in-hospital and post-hospital) were assessed. Results The incidence and mortality rates for respiratory diseases were 4-fold and 11-fold higher in 2020 compared to 2019 (877.5 vs 228.2 and 11.2 vs 1.2 per 100,000 respectively). The PCR-positive cases (compared to PCR-negative) had 2-fold higher risk of overall mortality. We observed 24% higher risk of death in males compared to females and in older patients compared to younger ones. Patients residing in rural areas had 66% higher risk of death compared to city residents and being treated in a provisional hospital was associated with 1.9-fold increased mortality compared to those who were treated in infectious disease hospitals. Conclusion This is the first study from the Central Asia and Eurasia regions, evaluating the mortality of SARS-CoV-2 PCR-positive and PCR-negative respiratory system diseases during the peak of COVID-19 pandemic. We describe a higher mortality rate for PCR-test positive cases compared to PCR-test negative cases, for males compared to females, for elder patients compared to younger ones and for patients living in rural areas compared to city residents.

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Section: Discussionmentioning

confidence: 99%

SARS-CoV-2 PCR-positive and PCR-negative cases of pneumonia admitted to the hospital during the peak of COVID-19 pandemic: analysis of in-hospital and post-hospital mortality

et al. 2021

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“…There is a scarcity of modeling studies aimed at elucidating the transmission dynamics and control in Eastern Europe, where the post-socialist economies and the corresponding healthcare systems are inherently different from Western Europe. The available transmission models for Eastern Europe are based on relatively simple SIR or similar compartmental models [31][32][33] where individuals are assigned to groups and all individuals within a given group are expected to have the same characteristics. However, no agent-based modeling studies have been conducted to evaluate the impact of spatial heterogeneities in key transmission drivers such as population density [32][33] [34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Investigating the first stage of the COVID-19 pandemic in Ukraine using epidemiological and genomic data

Gankin¹,

Nemira

Koniukhovskii³

et al. 2021

Preprint

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The novel coronavirus SARS-CoV-2 emerged in China in December 2019 and has rapidly spread around the globe. The World Health Organization declared COVID-19 a pandemic in March 2020 just three months after the introduction of the virus. Individual nations have implemented and enforced with varying degrees of success a variety of social distancing interventions to slow the virus spread. Investigating the role of non-pharmaceutical interventions on COVID-19 transmission in different settings is an important research. While most transmission modeling studies have focused on the dynamics in China, neighboring Asian counties, Western Europe, and North America, there is a scarcity of studies for Eastern Europe. This study starts to fill this gap by analyzing the characteristics of the first epidemic wave in Ukraine using mathematical and statistical models together with epidemiological and genomic sequencing data. Using an agent-based model, the trajectory of the first wave in terms of cases and deaths and explore the impact of quarantine strategies via simulation studies have been characterized. The implemented stochastic model for epidemic counts suggests, that even a small delay of weeks could have increased the number of cases by up to 50\%, with the potential to overwhelm hospital systems. The genomic data analysis suggests that there have been multiple introductions of SARS-CoV-2 into Ukraine during the early stages of the epidemic with eight distinct transmission clusters identified. The basic reproduction number for the epidemic has been estimated independently both from case counts data and from genomic data. The findings support the hypothesis that, the public health measures did not have a decreasing effect on the existing viral population number at the time of implementation, since strains were detected after the quarantine date. However, the public health measures did help to prevent the appearance of new (and potentially more virulent) SARS-CoV-2 variants in Ukraine.

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“…Here we are assuming a uniform lockdown, while some studies consider different kind of lockdown, see e.g. [4] where some individuals are separated from S into protected compartment.…”

Section: Methodsmentioning

confidence: 99%

Mathematical Modelling the Impact Evaluation of Lockdown on Infection Dynamics of COVID-19 in Italy

Kadyrov

Orynbassar

et al. 2020

Preprint

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The Severe Acute Respiratory Syndrome Coronavirus 2 (SARSCoV-2), the cause of the coronavirus disease-2019 (COVID-19), within months of emergence from Wuhan, China, has rapidly spread, exacting a devastating human toll across around the world reaching the pandemic stage at the the beginning of March 2020. Thus, COVID-19s daily increasing cases and deaths have led to worldwide lockdown, quarantine and some restrictions. Covid-19 epidemic in Italy started as a small wave of 2 infected cases on January 31. It was followed by a bigger wave mainly from local transmissions reported in 6387 cases on March 8. It caused the government to impose a lockdown on 8 March to the whole country as a way to suppress the pandemic. This study aims to evaluate the impact of the lockdown and awareness dynamics on infection in Italy over the period of January 31 to July 17 and how the impact varies across different lockdown scenarios in both periods before and after implementation of the lockdown policy. The findings SEIR reveal that implementation lockdown has minimised the social distancing flattening the curve. The infections associated with COVID-19 decreases with quarantine initially then easing lockdown will not cause further increasing transmission until a certain period which is explained by public high awareness. Completely removing lockdown may lead to sharp transmission second wave. Policy implementation and limitation of the study were evaluated at the end of the paper. Keywords COVID-19 - Lockdown - Epidemic model - SEIR - Awareness - Dynamical systems.

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COVID-19 Outbreak in Post-Soviet States: Modeling the Best and Worst Possible Scenarios

Cited by 8 publications

References 21 publications

SARS-CoV-2 PCR-positive and PCR-negative cases of pneumonia admitted to the hospital during the peak of COVID-19 pandemic: analysis of in-hospital and post-hospital mortality

SARS-CoV-2 PCR-positive and PCR-negative cases of pneumonia admitted to the hospital during the peak of COVID-19 pandemic: analysis of in-hospital and post-hospital mortality

Investigating the first stage of the COVID-19 pandemic in Ukraine using epidemiological and genomic data

Mathematical Modelling the Impact Evaluation of Lockdown on Infection Dynamics of COVID-19 in Italy

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