COVID-19 epidemic in Malaysia: Impact of lock-down on infection dynamics

Naomie Salim; Weng Howe Chan; Shuhaimi Mansor; Nor Erne Nazira Bazin; Safiya Amaran; Ahmad Athif Mohd Faudzi; Anazida Zainal; Sharin Hazlin Huspi; Eric Jiun Hooi Khoo; Shaekh Mohammad Shithil

Many models have been used to predict the outbreak pattern of COVID-19 epidemic. Several models used the normal distribution as a model of the COVID-19 epidemic and to forecast peak hospital load [13] . A curve-fitting tool to fit a nonlinear mixed effects model was developed based on available data. For instance, the cumulative rate is assumed to follow a parameterized Gaussian error function where the function is the Gaussian error function. Parameters such as death rate, the time since death rate exceeded a certain number was used as a location-specific inflection point and location-specific growth parameter, have been used. Other sigmoidal functional forms were also considered. Data was also fit to the log of the death rate in the available data, using an optimization framework. The logistic distribution is based on a continuous probability distribution which resembles the normal distribution in shape but has heavier tails. A generalized logistic growth model was used by [14] , together with the Richards growth model and a sub-epidemic wave model to generate COVID-19 10-day forecasts for Guangdong and Zhejiang. The generalized logistic growth model and the Richards model extend the simple logistic growth model with an additional scaling parameter. The sub-epidemic model accommodates complex epidemic trajectories by assembling the contribution of inferred overlapping sub-epidemics. The model was fit to the "incidence" curve and the best-fit solution for each model was estimated using nonlinear least squares fitting so that model parameters minimizes the sum of squared errors between the model and the data. A parametric bootstrap approach was used to generate uncertainty bounds around the best-fit solution assuming a Poisson error structure.