Abstract:
Introduction: According to the Oxford Medical Dictionary, Corona virus is the largest known viral RNA genome and causes devastating epizootics in livestock and poultry. Human corona viruses cause upper respiratory tract infections and severe acute respiratory syndrome (SARS). The initiative for this study was the extreme life threatening nature of this virus and the global pandemic it has caused. The responses were taken to be the number of deaths, number of recoveries and the number sick with the disease at a particular point in time, globally and the explanatory variables were climate variables.
Method: This is a survey type of study as the data has been extracted over a short period of time and the sampling method adopted is post cluster sampling. Simple descriptive statistics, clustering and generalized linear mixed models have been used for modelling.
Results: There was a strong regional effect of over three which was highly significant for every Covid 19 response. The air quality and temperature interaction and the air quality and humidity interaction were associated with the count of death at 0.0298 and 0.0027 levels of significance respectively. The count recovered was strongly associated with the temperature and humidity interaction and air quality at significance levels of 0.0002 and <0.0001 respectively. The Count at risk was strongly associated with the temperature, wind speeds and air quality three way interaction and this was significant at 0.0005 level.
Discussion: All four weather parameters effected one or more of the Covid 19 responses. The plots of Student residuals versus fitted values showed well-fitting models. The results of this research is useful in planning health care and allocating resources according to the region and the climate during a particular period.
