Machine Learning Impact Assessment Of Climate Factors On Daily Covid-19 Cases (preprint)

Coronavirus disease 2019 (also known as COVID-19) is a vastly infectious virus instigated by the coronavirus-2, which causes severe acute respiratory illness (SARS-Cov-2). Scientists and researchers are conducting a number of studies to better understand the COVID-19 pandemic's behavioral nature and spread, and machine learning provides useful tools. We used machine learning techniques to study the effect of climate conditions on daily instances of COVID-19 in this study. The study has three main objectives: first, to investigate the most climatic features that could affect the spread of novel COVID-19 cases; second, to assess the influence of government strategies on COVID-19 using our dataset; third, to do a comparative analysis of two different machine learning models, and develop a model to predict accurate response to the most features on COVID-19 spread. The goal of this research is to assist health-care facilities and governments with planning and decision-making. The study compared random forest and artificial neural network models for analysis. In addition, feature importance among the independent variables (climate variables) were identified with the random forest. The study used publicly available datasets of COVID-19 cases from the World Health Organization and climate variables from National Aeronautics and Space Administration websites respectively. Our results showed that relative humidity and solar had significant impact as a feature of weather variables on COVID-19 recorded cases; and that random forest predicted accurate response to the most climatic features on COVID-19 spread. Based on this, we propose the random forest model to predict COVID-19 cases using weather variables.

MACHINE LEARNING IMPACT ASSESSMENT OF CLIMATE FACTORS ON DAILY COVID-19 CASES (preprint)

Coronavirus disease 2019 (also known as COVID-19) is a vastly infectious virus instigated by the coronavirus-2, which causes severe acute respiratory illness (SARS-Cov-2). Scientists and researchers are conducting a number of studies to better understand the COVID-19 pandemic's behavioral nature and spread, and machine learning provides useful tools. We used machine learning techniques to study the effect of climate conditions on daily instances of COVID-19 in this study. The study has three main objectives: first, to investigate the most climatic features that could affect the spread of novel COVID-19 cases; second, to assess the influence of government strategies on COVID-19 using our dataset; third, to do a comparative analysis of two different machine learning models, and develop a model to predict accurate response to the most features on COVID-19 spread. The goal of this research is to assist health-care facilities and governments with planning and decision-making. The study compared random forest and artificial neural network models for analysis. In addition, feature importance among the independent variables (climate variables) were identified with the random forest. The study used publicly available datasets of COVID-19 cases from the World Health Organization and climate variables from National Aeronautics and Space Administration websites respectively. Our results showed that relative humidity and solar had significant impact as a feature of weather variables on COVID-19 recorded cases; and that random forest predicted accurate response to the most climatic features on COVID-19 spread. Based on this, we propose the random forest model to predict COVID-19 cases using weather variables.

Effects of weather and policy intervention on COVID-19 infection in Ghana (preprint)

Even though laboratory and epidemiological studies have demonstrated the effects of ambient temperature on the transmission and survival of coronaviruses, not much has been done on the effects of weather on the spread of COVID-19. This study investigates the effects of temperature, humidity, precipitation, wind speed and the specific government policy intervention of partial lockdown on the new cases of COVID-19 infection in Ghana. Daily data on confirmed cases of COVID-19 from March 13, 2020 to April 21, 2020 were obtained from the official website of Our World in Data (OWID) dedicated to COVID-19 while satellite climate data for the same period was obtained from the official website of NASA's Prediction of Worldwide Energy Resources (POWER) project. Considering the nature of the data and the objectives of the study, a time series generalized linear model which allows for regressing on past observations of the response variable and covariates was used for model fitting. The results indicate significant effects of maximum temperature, relative humidity and precipitation in predicting new cases of the disease. Also, results of the intervention analysis indicate that the null hypothesis of no significant effect of the specific policy intervention of partial lockdown should be rejected (p-value=0.0164) at a 5\% level of significance. These findings provide useful insights for policymakers and the public.