COVID-19 Pandemic Data Modeling in Pakistan Using Time-Series SIR

Area of Science:

• Epidemiology
• Mathematical Modeling

Background:

• Pakistan is experiencing its fourth wave of COVID-19.
• Existing epidemiological models have not been extensively applied to Pakistan's COVID-19 data.

Purpose of the Study:

• To analyze Pakistan's COVID-19 data using susceptible, infected, and recovered (SIR) epidemiological models.
• To evaluate the suitability of the standard 14-day coronavirus incubation period for Pakistan.
• To assess the pandemic status of COVID-19 in Pakistan using the basic reproduction number (R0).

Main Methods:

• Utilized Bayesian and time-series SIR (tSIR) approaches to model Pakistan's COVID-19 data.
• Compared the performance of posterior-based SIR (pSIR) models with different priors and log-likelihood distributions.
• Analyzed the reporting rate (ρ) from the tSIR model to infer case underreporting.

Main Results:

• The standard 14-day incubation period assumption is not appropriate for Pakistan's COVID-19 data.
• COVID-19 is not classified as a pandemic in Pakistan, unlike in several other countries, attributed to effective government control strategies.
• The pSIR model with a uniform prior for R0 and a Poisson log-likelihood distribution yielded superior results.
• The tSIR model indicated a reporting rate (ρ) less than 1, signifying underreported COVID-19 cases.

Conclusions:

• The findings challenge the universal applicability of standard epidemiological assumptions for COVID-19 in Pakistan.
• Effective public health strategies in Pakistan have potentially mitigated the pandemic's severity.
• There is a significant underreporting of COVID-19 cases in Pakistan, necessitating improved surveillance and reporting mechanisms.