Adjusting non-pharmaceutical interventions based on hospital bed capacity using a multi-operator differential evolution (preprint)

Without vaccines and medicine, non-pharmaceutical interventions (NPIs) such as social distancing, have been the main strategy in controlling the spread of COVID-19. Strict social distancing policies may lead to heavy economic losses, while relaxed social distancing policies can threaten public health systems. We formulate an optimization problem that minimizes the stringency of NPIs during the prevaccination and vaccination phases and guarantees that cases requiring hospitalization will not exceed the number of available hospital beds. The approach utilizes an SEIQR model that separates mild from severe cases and includes a parameter that quantifies NPIs. Payoff constraints ensure that daily cases are decreasing at the end of the prevaccination phase and cases are minimal at the end of the vaccination phase. Using the penalty method, the constrained minimization is transformed into a non-convex, multi-modal unconstrained optimization problem, which is solved using a metaheuristic algorithm called the improved multi-operator differential evolution. We apply the framework to determine optimal social distancing strategies in the Republic of Korea given different amounts and types of antiviral drugs. The model considers variants, booster shots, and waning of immunity. The optimal values show that fast administration of vaccines is as important as using highly effective vaccines. The initial number of infections and daily imported cases should be kept minimum especially if the severe bed capacity is low. In Korea, a gradual easing of NPIs without exceeding the severe bed capacity is possible if there are at least seven million antiviral drugs and the effectiveness of the drug in reducing disease severity is at least 86%. Model parameters can be adapted to a specific region or country, or other infectious disease. The framework can also be used as a decision support tool in planning practical and economic policies, especially in countries with limited healthcare resources.

A Model-Based Strategy on COVID-19 Vaccine Roll-out in the Philippines (preprint)

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2. Millions of people have fallen sick, and some have died due to this affliction that has spread across the globe. The current pandemic has disrupted normal day-to-day human life, causing a profound social and economic burden. Vaccination is an important control measure that could significantly reduce the incidence of cases and mortality if properly and efficiently distributed. In this work, an age-structured model of COVID-19 transmission, incorporating an unreported infectious compartment, is developed. Three age groups are considered, namely: young (0-19 years), adult (20-64 years), and elderly (65+ years). The transmission and reporting rates are determined for each group by utilizing the number of COVID-19 cases in the National Capital Region in the Philippines. Optimal control theory is employed to identify the best vaccine allocation to different age groups. Further, three different vaccination periods are considered to reflect phases of vaccination priority groups: the first, second, and third account for the inoculation of the elderly, adult and elderly, and all three age groups, respectively. This study could guide in making informed decisions in mitigating a population-structured disease transmission under limited resources.

Modeling behavior change and underreporting in the early phase of COVID-19 pandemic in Metro Manila, Philippines (preprint)

When the Philippine government eased the community quarantine restrictions on June 2020, the healthcare system was overwhelmed by the surge in coronavirus disease 2019 (COVID-19) cases. In this study, we developed an SEIQR model considering behavior change and unreported cases to examine their impact on the COVID-19 case reports in Metro Manila during the early phase of the pandemic. We found that if behavior was changed one to four weeks earlier, then the cumulative number of cases can be reduced by up to 74% and the peak delayed by up to four weeks. Moreover, a two- or threefold increase in the reporting ratio can decrease the cumulative number of cases by 29% or 47%, respectively, at the end of September 2020. Results of our finding are expected to guide healthcare professionals to mitigate disease spread and minimize socioeconomic burden of strict lockdown policies during the start of an epidemic.

Modelling The Effects of Social Distancing, Antiviral Therapy, And Booster Shots On Mitigating Omicron Spread (preprint)

We propose a COVID-19 mathematical model that considers Omicron and previous variants, booster shots, waning, breakthrough infections, and antiviral therapy. We quantify the effects of social distancing (SD) in the Republic of Korea by estimating the reduction in transmission µ induced by government policies from February 26, 2021 to January 16, 2022. The time-dependent µ has a value between 0 and 1, with 1 being the strictest SD. Simulations show that by February 28, 2022, 92% of infections are caused by Omicron. Strict SD (µ = 0.81) is necessary to reduce the number of cases. However, if the focus is shifted towards reducing the severe instead of daily cases, relaxed SD (µ = 0.66) is possible if the administered booster shots have at least 90% effectiveness. Furthermore, if the available antiviral pill is at least 89% effective against severe infections with Omicron, then a more relaxed SD (µ = 0.54) can be implemented.

Quantifying the effects of non-pharmaceutical and pharmaceutical interventions against COVID-19 epidemic in the Republic of Korea: Mathematical model-based approach considering age groups and the Delta variant (preprint)

Background: Early vaccination efforts and non-pharmaceutical interventions were insufficient to prevent a surge of coronavirus disease 2019 (COVID-19) cases triggered by the Delta variant. This study aims to understand how vaccination and variants contribute to the spread of COVID-19 so that appropriate measures are implemented. Methods: A compartment model that includes age, vaccination, and infection with the Delta or non-Delta variants was developed. We estimated the transmission rates using maximum likelihood estimation and phase-dependent reduction effect of non-pharmaceutical interventions (NPIs) according to government policies from 26 February to 8 October 2021. We extended our model simulation until 31 December considering the initiation of eased NPIs. Furthermore, we also performed simulations to examine the effect of NPIs, arrival timing of Delta variant, and speed of vaccine administration. Results: The estimated transmission rate matrices show distinct pattern, with the transmission rates of younger age groups (0~39 years) much larger than non-Delta. Social distancing (SD) level 2 and SD4 in Korea were associated with transmission reduction factors of 0.64 to 0.69 and 0.70 to 0.78, respectively. The easing of NPIs to a level comparable to SD2 should be initiated not earlier than 16 October to keep the number of severe cases below the capacity of Korean healthcare system. Simulation results also showed that a surge prompted by the spread of the Delta variant can be prevented if the number of people vaccinated daily was larger. Conclusions: Simulations showed that the timing of easing and intensity of NPIs, vaccination speed, and screening measures are key factors in preventing another epidemic wave.