Quasi-Poisson model

The quasi-Poisson regression is considered as s typical model for modeling over distributed count variables (Imai et al. 2015). Recently a log-linear quasi-Poisson regression model is used to correlate the climate variables and daily counts ( Hridoy et al. 2021). The quasi-Poisson model as a function of the meteorological parameters can be given by the following equation:

where log(U) refers to the logarithmic of the daily new cases, Y is the fitting model, β0 is the overall coefficient, and α_o, α₁(T_C), α₂ (hum), α₃ (DP), and α₄ (v_wind) represent coefficients for mean temperature (°C), relative humidity (%), dew point (°C), and wind speed (km/h) respectively. DOW refers to a variable that indicates the day of the week to account for any seasonal effects that might exist or a changing trend over time. LD = 1 for lockdown days and 0 otherwise. Log(P) is the log of population density.