The Impact of Strict Public Health Measures on COVID-19 Transmission in Developing Countries: The Case of Kuwait

Abdullah A Al-Shammari 1 2, Hamad Ali 2 3, Barrak Alahmad 4, Faisal H Al-Refaei 2, Salman Al-Sabah 5 6, Mohammad H Jamal 5 6, Abdullah Alshukry 7, Qais Al-Duwairi 2, Fahd Al-Mulla 2

Affiliations

01 November 2021

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doi: 10.3389/fpubh.2021.757419

Abstract

Background: Many countries have succeeded in curbing the initial outbreak of COVID-19 by imposing strict public health control measures. However, little is known about the effectiveness of such control measures in curbing the outbreak in developing countries. In this study, we seek to assess the impact of various outbreak control measures in Kuwait to gain more insight into the outbreak progression and the associated healthcare burden. Methods: We use a SEIR mathematical model to simulate the first wave of the epidemic outbreak of COVID-19 in Kuwait with additional testing and hospitalization compartments. We calibrate our model by using a NBD observational framework for confirmed case and death counts. We simulate trajectories of model forecasts and assess the effectiveness of public health interventions by using maximum likelihood to estimate both the basic and effective reproduction numbers. Results: Our results indicate that the early strict control measures had the effect of delaying the intensity of the outbreak but were unsuccessful in reducing the effective reproduction number below 1. Forecasted model trajectories suggest a need to expand the healthcare system capacity to cope with the associated epidemic burden of such ineffectiveness. Conclusion: Strict public health interventions may not always lead to the same desired outcomes, particularly when population and demographic factors are not accounted for as in the case in some developing countries. Real-time dynamic modeling can provide an early assessment of the impact of such control measures as well as a forecasting tool to support outbreak surveillance and the associated healthcare expansion planning.

Keywords: COVID-19; epidemic outbreak; forecasting; healthcare demand; infectious disease; mathematical modeling; public health.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1 Cumulative number of reported COVID-19 cases in Kuwait along with a timeline of events.

Figure 1 Cumulative number of reported COVID-19 cases in Kuwait along with a timeline of events.

Figure 2 Schematic diagram of the COVID-19 transmission model. Individuals (S) susceptible to the virus become infected by infectious individuals (I). They then move through a latent period (E) before becoming infectious (I). Infectious individuals can either move through a detection period (C) or eventually recover without symptoms. Confirmed infectious individuals move through an initial hospitalization period (H) after which they are admitted to either an isolation ward (W) or an intensive care unit (U). Intensive care patients may recover and be sent to an isolation ward W or ultimately die (D). Isolated patients move through a recovery period (R), where they are assumed to be immune to the disease, at least in the medium term.

Figure 2 Schematic diagram of the COVID-19 transmission model. Individuals (S) susceptible to the virus become infected by infectious individuals (I). They then move through a latent period (E) before becoming infectious (I). Infectious individuals can either move through a detection period (C) or eventually recover without symptoms. Confirmed infectious individuals move through an initial hospitalization period (H) after which they are admitted to either an isolation ward (W) or an intensive care unit (U). Intensive care patients may recover and be sent to an isolation ward W or ultimately die (D). Isolated patients move through a recovery period (R), where they are assumed to be immune to the disease, at least in the medium term.

Figure 3 Observed and forecasted trajectories assuming 500,000 unprotected susceptible individuals. Observed and projected daily numbers of (A) incident infections, (B) death cases, (C) general hospital admissions, and (D) ICU admissions. Red rectangular ribbon highlights the projected time-window of the epidemic peak. Red lines represent the reported data. Black dashed lines represent model projections based on MLE of unknown parameters with shaded ribbons representing 95% credible interval on new observations. We note here that the observed cases and their projections only represent a fraction of the actual and model prevalence. This is based on our assumption of under-reporting and the presence of asymptomatic individuals in the population.

Figure 3 Observed and forecasted trajectories assuming 500,000 unprotected susceptible individuals. Observed and projected daily numbers of (A) incident infections, (B) death cases, (C) general hospital admissions, and (D) ICU admissions. Red rectangular ribbon highlights the projected time-window of the epidemic peak. Red lines represent the reported data. Black dashed lines represent model projections based on MLE of unknown parameters with shaded ribbons representing 95% credible interval on new observations. We note here that the observed cases and their projections only represent a fraction of the actual and model prevalence. This is based on our assumption of under-reporting and the presence of asymptomatic individuals in the population.

Figure 4 Tripling the size of the unprotected susceptible population. Peaks of forecasted trajectories are approximately tripled in size. The projected time-window of the peak is delayed by 2-weeks and widened (3-week period). Observed and projected daily numbers of (A) incident infections, (B) death cases, (C) general hospital admissions, and (D) ICU admissions. Red rectangular ribbon highlights the projected time-window of the epidemic peak. Red lines represent the reported data. Black dashed lines represent model projections based on MLE of unknown parameters with shaded ribbons representing 95% credible interval on new observations. We note here that the observed cases and their projections only represent a fraction of the actual and model prevalence. This is based on our assumption of under-reporting and the presence of asymptomatic individuals in the population.

Figure 4 Tripling the size of the unprotected susceptible population. Peaks of forecasted trajectories are approximately tripled in size. The projected time-window of the peak is delayed by 2-weeks and widened (3-week period). Observed and projected daily numbers of (A) incident infections, (B) death cases, (C) general hospital admissions, and (D) ICU admissions. Red rectangular ribbon highlights the projected time-window of the epidemic peak. Red lines represent the reported data. Black dashed lines represent model projections based on MLE of unknown parameters with shaded ribbons representing 95% credible interval on new observations. We note here that the observed cases and their projections only represent a fraction of the actual and model prevalence. This is based on our assumption of under-reporting and the presence of asymptomatic individuals in the population.

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