Characterizing the COVID‐19 dynamics with a new epidemic model: Susceptible‐exposed‐asymptomatic‐symptomatic‐active‐removed

Modeling the health impact of increasing vaccine coverage and nonpharmaceutical interventions against coronavirus disease 2019 in Ghana

Seroprevalence studies assessing community exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Ghana concluded that population-level immunity remained low as of February 2021. Thus, it is important to demonstrate how increasing vaccine coverage reduces the economic and public health impacts associated with SARS-CoV-2 transmission. To that end, this study used a Susceptible-Exposed-Presymptomatic-Symptomatic-Asymptomatic-Recovered-Dead-Vaccinated compartmental model to simulate coronavirus disease 2019 (COVID-19) transmission and the role of public health interventions in Ghana. The impact of increasing vaccination rates and decline in transmission rates due to nonpharmaceutical interventions (NPIs) on cumulative infections and deaths averted was explored under different scenarios. Latin hypercube sampling-partial rank correlation coefficient (LHS-PRCC) was used to investigate the uncertainty and sensitivity of the outcomes to the parameters. Simulation results suggest that increasing the vaccination rate to achieve 50% coverage was associated with almost 60,000 deaths and 25 million infections averted. In comparison, a 50% decrease in the transmission coefficient was associated with the prevention of about 150,000 deaths and 50 million infections. The LHS-PRCC results indicated that in the context of vaccination rate, cumulative infections and deaths averted were most sensitive to vaccination rate, waning immunity rates from vaccination, and waning immunity from natural infection. This study's findings illustrate the impact of increasing vaccination coverage and/or reducing the transmission rate by NPI adherence in the prevention of COVID-19 infections and deaths in Ghana.

A new approach to modeling pre-symptomatic incidence and transmission time of imported COVID-19 cases evolving with SARS-CoV-2 variants

There is paucity of the statistical model that is specified for data on imported COVID-19 cases with the unique global information on infectious properties of SARS-CoV-2 variant different from local outbreak data used for estimating transmission and infectiousness parameters via the established epidemic models. To this end, a new approach with a four-state stochastic model was proposed to formulate these well-established infectious parameters with three new parameters, including the pre-symptomatic incidence rate, the median of pre-symptomatic transmission time (MPTT) to symptomatic state, and the incidence (proportion) of asymptomatic cases using imported COVID-19 data. We fitted the proposed stochastic model to empirical data on imported COVID-19 cases from D614G to Omicron with the corresponding calendar periods according to the classification GISAID information on the evolution of SARS-CoV-2 variant between March 2020 and Jan 2022 in Taiwan. The pre-symptomatic incidence rate was the highest for Omicron followed by Alpha, Delta, and D614G. The MPTT (in days) increased from 3.45 (first period) ~ 4.02 (second period) of D614G until 3.94-4.65 of VOC Alpha but dropped to 3.93-3.49 of Delta and 2 days (only first period) of Omicron. The proportion of asymptomatic cases increased from 29% of D-614G period to 59.2% of Omicron. Modeling data on imported cases across strains of SARS-CoV-2 not only bridges the link between the underlying natural infectious properties elucidated in the previous epidemic models and different disease phenotypes of COVID-19 but also provides precision quarantine and isolation policy for border control in the face of various emerging SRAS-CoV-2 variants globally.

Estimation of the COVID-19 Mean Incubation Time: Systematic Review, Meta-analysis, and Sensitivity Analysis

Providing sensible estimates of the mean incubation time for COVID‐19 is important yet complex. This study aims to provide synthetic estimates of the mean incubation time of COVID‐19 by capitalizing on available estimates reported in the literature and exploring different ways to accommodate heterogeneity involved in the reported studies. Online databases between January 1, 2020 and May 20, 2021 are first searched to obtain estimates of the mean incubation time of COVID‐19, and meta‐analyses are then conducted to generate synthetic estimates. Heterogeneity of the studies is examined via the use of Cochran's Q statistic and Higgin's & Thompson's I2 statistic, and subgroup analyses are conducted using mixed effects models. The publication bias issue is assessed using the funnel plot and Egger's test. Using all those reported mean incubation estimates for COVID‐19, the synthetic mean incubation time is estimated to be 6.43 days with a 95% confidence interval (CI) [5.90, 6.96], and using all those reported mean incubation estimates together with those transformed median incubation estimates, the estimated mean incubation time is 6.07 days with a 95% CI [5.70, 6.45]. The reported estimates of the mean incubation time of COVID‐19 vary considerably due to multiple reasons, including heterogeneity and publication bias. To alleviate these issues, we take different angles to provide a sensible estimate of the mean incubation time of COVID‐19. Our analyses show that the mean incubation time of COVID‐19 between January 1, 2020 and May 20, 2021 ranges from 5.68 to 8.30 days.