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Infection dynamics of coronavirus disease 2019 (COVID-19)

JUNG, Sungmok 北海道大学

2022.03.24

概要

Background and Objectives: The first confirmed case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was reported in January 2020, and the transmission of its causative agent, coronavirus disease 2019 (COVID-19) has rapidly spread around the world. In the early phase of the epidemic, knowledge of the confirmed case fatality risk (cCFR) and basic reproduction number (𝑅0) are crucial to characterize the severity and determine the pandemic potential of an emerging infectious disease. Thus, in order to assess the risk of COVID-19, Chapter 1 statistically estimated these two epidemiological measurements of COVID-19, using the exported cases which allow for an estimate of the cumulative number of SARS-CoV-2 infections in mainland China. In addition, Chapter 2 explored prospective exit strategies by projecting a second wave of the COVID-19 epidemic in Japan with different levels of restriction to suggest a more sustainable strategy than the current restrictive guideline. Lastly, the effective reproduction number (𝑅𝑡) has been used as an essential indicator for assessing the effectiveness of countermeasures during the COVID-19 pandemic. However, conventional methods relying on the reported case counts are unable to provide timely 𝑅𝑡 due to the time delay from infection to report. Chapter 3 suggested a simple statistical framework for predicting 𝑅𝑡 in near real-time, using timely accessible data of possible driving factors of SARS-CoV-2 transmissions (i.e., human mobility, temperature, and risk awareness). Chapter 1: Real-time estimation of the risk of death from COVID-19: inference using exported cases

Methods: Using the exponential growth rate of the estimated cumulative incidence from exportation cases and accounting for the time delay from illness onset to death, the cCFR and 𝑅0 were estimated. We modelled epidemic growth either from a single reported index case with illness onset on 8 December 2019 (Scenario 1) and using the growth rate fitted along with the other parameters (Scenario 2) based on 20 exported cases reported by 24 January 2020. Results: The cumulative incidence in China by 24 January was estimated at 6,924 cases (95% confidence interval [CI]: 4,885–9,211) and 19,289 cases (95% CI: 10,901–30,158), respectively. The latest estimated values of the cCFR were 5.3% (95% CI: 3.5%–7.5%) for Scenario 1 and 8.4% (95% CI: 5.3%–12.3%) for Scenario 2. The 𝑅0 was estimated to be 2.1 (95% CI: 2.0–2.2) and 3.2 (95% CI: 2.7–3.7) for Scenarios 1 and 2, respectively. Discussion: Based on these estimates, we argued that the current COVID-19 epidemic has a substantial potential for causing a pandemic. The proposed approach provides insights into early risk assessment using publicly available data. Chapter 2: Projecting a second wave of COVID-19 in Japan with variable interventions in highrisk settings

Methods: We quantified the next-generation matrix, accounting for high- and low-risk settings of SARS-CoV-2 transmissions. Then, the matrix was used to project the future incidence in Tokyo and Osaka after the first state of emergency is lifted, presenting multiple post-emergency scenarios with different levels of restriction. Results: The 𝑅𝑡 for the increasing phase, the transition phase and the state-of-emergency phase in the first wave of the disease were estimated as 1.78 (95% credible interval (CrI): 1.73–1.82), 0.74 (95% CrI: 0.71–0.78) and 0.63 (95% CrI: 0.61–0.65), respectively, in Tokyo and as 1.58 (95% CrI: 1.51–1.64), 1.20 (95% CrI: 1.15–1.25) and 0.48 (95% CrI: 0.44–0.51), respectively, in Osaka. Projections showed that a 50% decrease in the high-risk transmission is required to keep 𝑅𝑡 less than 1 in both locations—a level necessary to maintain control of the epidemic and minimize the burden of disease. Discussion: Compared with stringent interventions such as lockdowns, our proposed exit strategy from restrictive guidelines, focusing intervention efforts on the high-risk setting, allows socioeconomic activities to be maintained while minimizing the risk of a resurgence of the disease. Chapter 3: Predicting the effective reproduction number of COVID-19: inference using human mobility, temperature, and risk awareness.

Methods: A linear regression model to predict 𝑅𝑡 was designed and embedded in the renewal process. Four prefectures of Japan with high incidences in the first wave were selected for model fitting and validation. Predictive performance was assessed by comparing the observed and predicted incidences using cross-validation, and by testing on a separate dataset in two other prefectures with distinct geographical and climatological settings from the four studied prefectures. Results: The predicted mean values of 𝑅𝑡 and 95% uncertainty intervals followed the overall trends for incidence, while predictive performance was partially diminished when 𝑅𝑡changed abruptly, potentially due to superspreading events or when stringent countermeasures were implemented. In addition, the predictive performance of the best-ranked model on the separate test data indicates the applicability of the proposed model to other geographical settings.

Discussion: The described model can potentially be used for monitoring the transmission dynamics of COVID-19 ahead of the formal estimates, subject to time delay, providing essential information for timely planning and assessment of countermeasures.

Conclusion: Since the SARS-CoV-2 emerged, it has posed an enormous threat to healthcare systems all around the world. The present dissertation has contributed to a better knowledge of COVID-19 infection dynamics, which is crucial for controlling the ongoing COVID-19 pandemic and devising timely and proper COVID-19 response strategies. First, it estimated the risk of death and transmissibility of COVID-19 for the early risk assessment. In addition, it projected the future dynamics of COVID-19 by reconstructing the next-generation matrix accounting for high- and low-risk transmission settings, and quantitatively assessed the impacts of possible exit strategies on the SARS-CoV-2 transmissions in Japan. Lastly, it suggested the statistical framework for providing timely prediction of the effective reproduction number, that can be used before a formal estimate is available. Despite uncertainties surrounding new SARSCoV-2 variants, these series of studies can shed light on a better understanding of the infection dynamics of COVID-19 and the establishment of evidence-based response strategies for minimizing the burden of the ongoing COVID-19 pandemic.

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