Estimation of country-level basic reproductive ratios for novel Coronavirus (COVID-19) using synthetic contact matrices

Abstract

The outbreak of novel coronavirus has the potential for global spread, infecting large numbers in all countries. In this case, estimating the country-specific basic reproductive ratio is a vital first step in public-health planning. The basic reproductive ratio (R0) is determined by both the nature of pathogen and the network of contacts through which the disease can spread -with this network determined by socio-demographics including age-structure and household composition. Here we focus on the age-structured transmission within the population, using data from China to inform agedependent susceptibility and synthetic age-mixing matrices to inform the contact network. This allows us to determine the country-specific basic reproductive ratio as a multiplicative scaling of the value from China. We predict that R0 will be highest across Eastern Europe and Japan, and lowest across Africa, Central America and South-Western Asia. This pattern is largely driven by the ratio of children to older adults in each country and the observed propensity of clinical cases in the elderly.

The far side of the COVID-19 epidemic curve: local re-openings and re-closings based on globally coordinated triggers may work best

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Prioritising COVID-19 vaccination in changing social and epidemiological landscapes

“…Many populations will already have experienced one or more waves of COVID-19. As a result of natural immunity, the effective reproduction number R ef f (the average number of secondary infections produced per infected person) will be reduced from its original value of approximately R 0 = 2.2 in the absence of pre-existing immunity (8). Epidemiological theory tells us that as R (or R 0 ) decline toward 1, the indirect benefits of transmission-blocking vaccines become stronger.…”

Neural Network Based Country Wise Risk Prediction of COVID-19

“…Yan et al [24] built a predictive model to identify early detection of high-risk patients before their health status is transformed from mild to critically ill. In recent times, numerous research articles have been published on epidemic prediction of the coronavirus pandemic [25][26][27][28][29][30][31][32][33][34]. Researchers designed new paradigms of AI-driven tools [35,36] that combine ML algorithms and different modalities of data.…”