No abstract

Akin to respiratory tract infection diseases, climatic conditions may significantly influence the COVID-19 epidemic. Since the beginning of the COVID-19 pandemic, significant efforts have been made to explore the relationship between climatic condition and growth in number of COVID-19 cases. Contentious findings of either positive, negative, or no association with climatic conditions have been reported in many studies based on some early data on COVID-19 cases over a shorter time span. We integrate COVID-19 datasets with long meteorological time series of 29 countries to explore cross-country variation in COVID-19 cases and death rates with respect to temperature and relative humidity. Our empirical study reveals that temperature and relative humidity jointly influence the growth of COVID-19 cases and death rates. We generate predictive scenarios for changes in daily cases and death rates under different combinations of temperature and relative humidity. Low temperature with low humidity in a temperate climate and high temperature with high humidity in a hot and humid climate are found to surge the growth of COVID-19 cases and death rates. These relationships and our predictive scenarios can be applied to generate early warning for any future outbreak to adopt stringency policies, kick-start economic activities, prepare healthcare service plans, and target vaccination coverage.

Corruption-income inequality nexus is likely to affect the healthcare services, which in turn affect a country’s ability to suppress an epidemic. Widespread corruption in public sectors may influence the data inventory practices to control the recording and sharing of official statistics to avoid political disturbance or social problems caused by an epidemic. This empirical study examines the effects of income inequality, data inventory, and universal healthcare coverage on cross-country variation in reported numbers of COVID-19 cases and deaths in the presence of corruption in public sectors. Daily numbers of COVID-19 cases and deaths of selected 29 countries are integrated for the first 120 days of the epidemic in each country. COVID-19 dataset is then integrated with a dataset of different indices. Fixed effect panel model is applied to explore the effects of corruption perception, income inequality, open data inventory practice, and universal health coverage on the daily numbers of COVID-19 cases and deaths per million. Income inequality, corruption perception and open data inventory are found to significantly affect the number of confirmed cases and deaths. Countries with alarming income inequality are found to report 39.89 more COVID-19 cases per million, on average. Under a lower level of corruption, countries with lower level of open data inventory are expected to report 74.31 more COVID-19 cases but 1.43 less deaths per million. Given a higher level of corruption, countries with lower level of open data inventory are expected to report lower number of COVID-19 cases and deaths. Corruption demonstrates a significant influence on the size of the epidemic in terms of the number of COVID-19 cases and deaths. A country with higher level of corruption in public sector along with lower levels of open data inventory is expected to report lower number of COVID-19 cases and deaths.

Distributed lags play important roles in explaining the short-run dynamic and long-run cumulative effects of features on a response variable. Unlike the usual lag length selection, important lags with significant weights are selected in a distributed lag model (DLM). Inspired by the importance of distributed lags, this research focuses on the construction of distributed lag inspired machine learning (DLIML) for predicting vaccine-induced changes in COVID-19 hospitalization and intensive care unit (ICU) admission rates. Importance of a lagged feature in DLM is examined by hypothesis testing and a subset of important features are selected by evaluating an information criterion. Akin to the DLM, we demonstrate the selection of distributed lags in machine learning by evaluating importance scores and objective functions. Finally, we apply the DLIML with supervised learning for forecasting daily changes in COVID-19 hospitalization and ICU admission rates in United Kingdom (UK) and United States of America (USA). A sharp decline in hospitalization and ICU admission rates are observed when around 40% people are vaccinated. For one percent more vaccination, daily changes in hospitalization and ICU admission rates are expected to reduce by 4.05 and 0.74 per million after 14 days in UK, and 5.98 and 1.04 per million after 20 days in USA, respectively. Long-run cumulative effects in the DLM demonstrate that the daily changes in hospitalization and ICU admission rates are expected to jitter around the zero line in a long-run. Application of the DLIML selects fewer lagged features but provides qualitatively better forecasting outcome for data-driven healthcare service planning.

Background: At the beginning of COVID-19 outbreak, very little was known about its control options and most of the intervention options relied mainly on other virus outbreak and influenza epidemic. Different countries started responding to this epidemic somewhat in different ways to achieve a common goal of transmission reduction. Population-wide intervention measures such as social distancing, testing and isolation were implemented in different countries. However, commonly adopted intervention measures impacted different countries in different ways. Differential effects of those interventions become apparent in Australia and Italy, where Australia's success to control the epidemic has been in limelight. Differences in time to and extent of widespread testing are likely to have differential effects on the daily number of confirmed cases in both countries.Methods: We apply panel generalized linear models for daily number of cases to explore differential effects of timing to and extent of widespread testing on daily number of cases. We have analyzed daily number of confirmed cases data from the first reported cases in Australia and Italy to 31 May 2020. Our data sets can be downloaded from an open source database at https://ourworldindata.org.Results: More tests during the early stage of outbreak prior to reach the peak may reduce the daily number of cases by almost 40%. Only 1% increase in test positivity on the (t-5)th day may incur 1.84% increase in daily number of cases on the t-th day. For 1% increase in test positivity rate on the (t-5)th day, a country with one unit higher logarithm of population density may result in 2.82 times higher number of cases on the t-th day.Conclusion: Conducting widespread testing during the early stage prior to reaching the peak has favored Australia to control the outbreak much faster than Italy. Early adoption of widespread testing with lower degree of test positivity rates flattens the curve faster. Population density has a moderating effect. Even if the test positivity rate is the same, a region with higher population density is likely to experience a peak with higher number of daily confirmed cases.