Changes in respiratory infection trends during the COVID-19 pandemic in the haematologic malignancy patients (preprint)

Background: The coronavirus disease 2019 (COVID-19) pandemic globally changed respiratory infection patterns. However, its impact on community-acquired pneumonia (CAP) in high risk patients with haematological malignancies (HM) is uncertain. We aimed to examine CAP aetiology changes in patients with HM pre- and post-COVID-19 pandemic. Methods: This retrospective study included 524 HM patients hospitalised with CAP between March 2018 and February 2022. Those who underwent bronchoscopy within 24 hours after admission to identify CAP aetiology were included. Data on patient characteristics, laboratory findings, and results of bronchioalveolar lavage fluid cultures and PCR tests were analysed to compare etiological changes and identify in-hospital mortality risk factors. Results: Patients were divided into pre-COVID-19 (44.5%) and post-COVID-19 (55.5%) groups. This study found a significant decrease in viral CAP in the post-COVID-19 era, particularly for influenza A, parainfluenza, adenovirus, and rhinovirus (3.0% vs. 0.3%, respectively, P = 0.036; 6.5% vs. 0.7%, respectively, P = 0.001; 5.6% vs. 1.4%, respectively, P = 0.015; 9.5% vs. 1.7%, respectively, P < 0.001). Bacterial, fungal, and unknown CAP aetiologies remain unchanged. Higher Sequential Organ Failure Assessment scores and lower platelet count correlated with in-hospital mortality after adjusting for potential confounding factors. Conclusion: The incidence of CAP in HM patients did not decrease after COVID-19. Additionally, CAP aetiology among patients with HM changed following the COVID-19 pandemic, with a significant reduction in viral pneumonia while bacterial and fungal pneumonia persisted. Further studies are required to evaluate the impact of COVID-19 on the prognosis of patients with HM and CAP.

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.

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.