The Second Wave of COVID-19 in South and Southeast Asia and the Effects of Vaccination

Antibiotic prescribing and bacterial infection in COVID-19 inpatients in Southeast Asia: a systematic review and meta-analysis

Background

The prescribing of antibiotics to treat COVID-19 patients has been observed to occur frequently, often without clear justification. This trend raises concerns that it may have exacerbated antimicrobial resistance (AMR). Despite longstanding concerns over AMR in Southeast Asian countries, data on this issue are notably lacking.

Objectives

To explore the impact of COVID-19 on antibiotic prescribing, bacterial infection prevalence and common resistant pathogens in COVID-19 inpatients.

Methods

We searched PubMed, EMBASE, Web of Science and ThaiJO (a Thai academic database) to identify studies conducted in ASEAN member countries and published between December 2019 and March 2023. Screening and data extraction were done by two independent reviewers, with results synthesized using random-effects meta-analyses and descriptive statistical analyses. This review was registered with PROSPERO (CRD42023454337).

Results

Of the 29 studies (19 750 confirmed COVID-19 cases) included for final analysis, the antibiotic prescribing rate was 62.0% (95%CI: 46.0%-76.0%) with a prescribing rate of 58.0% (21.0%-91.0%) in mild/moderate cases versus 91.0% (82.0%-98.0%) in severe/critical cases. Notably, 80.5% of antibiotics prescribed fall under the WHO AWaRe 'Watch' list, followed by 'Access' at 18.4% and 'Reserve' at 1.0%. The reported bacterial infection prevalence was 16.0% (7.0%-29.0%), with Acinetobacter baumannii being the most common resistant bacterium at 7.7%. Singapore was notable for its lower antibiotic prescribing rate of 17.0% and a lower bacterial infection rate of 10.0%.

Conclusions

High antibiotic prescribing rates, disproportionate to bacterial infections and varying practices for COVID-19 inpatients across countries highlight the urgent need for this region to collaborate to tackle and mitigate AMR.

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

BACKGROUND

The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19).

METHODOLOGY

PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542.

FINDINGS

In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias.

INTERPRETATION

The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.

Perceptions of COVID-19 during and after the Omicron outbreak among healthcare personnel in Indonesia

Introduction

The COVID-19 pandemic occurred in several waves with different levels of seriousness. Healthcare personnel (HCP) constituted a high-risk population for COVID-19, necessitating monitoring of their knowledge, attitudes, and practices (KAP) status and level of psychological distress. This study investigated differences in the impacts of COVID-19 during and after the Omicron outbreak among HCP in Indonesia.

Methods

An online structured questionnaire survey was distributed twice in selected hospitals of Indonesia: the first survey was between December 2021 and February 2022 (Omicron era) and the second between August and October 2022 (post-Omicron era). A multiple logistic regression model was used to determine the differences in KAP and psychological distress among HCP toward COVID-19 with demographic characteristics adjusted for.

Results

This study included 402 (Omicron era) and 584 (post-Omicron era) HCP members. Positive attitudes were more common in the Omicron era than in the post-Omicron era (p = 0.001). The availability of face shields and protective eyewear significantly decreased from 62.7 to 55.6% (p = 0.028). However, psychological distress among HCP significantly increased after the Omicron outbreak (p = 0.024). Multiple logistic regression analyses revealed a decrease of positive attitudes (OR = 0.626; 95% CI = 0.476-0.823) in the post-Omicron era.

Conclusion

Our data indicated a significant increase in psychological distress among HCP in the post-Omicron era. These findings suggest a need for greater focus on psychological distress among HCP in Indonesia.

The optimisation of public health emergency governance: a simulation study based on COVID-19 pandemic control policy

BACKGROUND

The outbreak of the COVID-19 pandemic sparked numerous studies on policy options for managing public health emergencies, especially regarding how to choose the intensity of prevention and control to maintain a balance between economic development and disease prevention.

METHODS

We constructed a cost-benefit model of COVID-19 pandemic prevention and control policies based on an epidemic transmission model. On this basis, numerical simulations were performed for different economies to analyse the dynamic evolution of prevention and control policies. These economies include areas with high control costs, as seen in high-income economies, and areas with relatively low control costs, exhibited in upper-middle-income economies.

RESULTS

The simulation results indicate that, at the outset of the COVID-19 pandemic, both high-and low-cost economies tended to enforce intensive interventions. However, as the virus evolved, particularly in circumstances with relatively rates of reproduction, short incubation periods, short spans of infection and low mortality rates, high-cost economies became inclined to ease restrictions, while low-cost economies took the opposite approach. However, the consideration of additional costs incurred by the non-infected population means that a low-cost economy is likely to lift restrictions as well.

CONCLUSIONS

This study concludes that variations in prevention and control policies among nations with varying income levels stem from variances in virus transmission characteristics, economic development, and control costs. This study can help researchers and policymakers better understand the differences in policy choice among various economies as well as the changing trends of dynamic policy choices, thus providing a certain reference value for the policy direction of global public health emergencies.

Wastewater surveillance of SARS-CoV-2 and chemical markers in campus dormitories in an evolving COVID - 19 pandemic

In this study, we report the implementation of a comprehensive wastewater surveillance testing program at a university campus in Singapore to identify Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infected individuals and the usage of pharmaceuticals and personal care products (PPCPs) as well as other emerging contaminants (ECs). This unique co-monitoring program simultaneously measured SARS-CoV-2 with chemical markers/contaminants as the COVID-19 situation evolved from pandemic to endemic stages, following a nationwide mass vaccination drive. SARS-CoV-2 RNA concentrations in wastewater from campus dormitories were measured using real-time reverse transcription-polymerase chain reaction (RT-qPCR) and corroborated with the number of symptomatic COVID-19 cases confirmed with the antigen rapid test (ART). Consistent results were observed where the concentrations of SARS-CoV-2 RNA detected in wastewater increased proportionately with the number of COVID-19 infected individuals residing on campus. Similarly, a wide range of ECs, including disinfectants and antibiotics, were detected through sensitive liquid chromatography with tandem mass spectrometry (LC-MS/MS) techniques to establish PPCPs consumption patterns during various stages of the COVID-19 pandemic in Singapore. Statistical correlation of SARS-CoV-2 RNA was observed with few ECs belonging to disinfectants, PCPs and antibiotics. A high concentration of disinfectants and subsequent positive correlation with the number of reported cases on the university campus indicates that disinfectants could serve as a chemical marker during such unprecedented times.

COVID-19 in patients with pre-existing chronic liver disease - predictors of outcomes

Coronavirus disease 2019 (COVID-19) has affected patients with pre-existing chronic liver disease (CLD) in various ways. The maximum impact was seen on patients with underlying cirrhosis who have shown to have poor clinical outcomes in the form of increased risk of hepatic decompensation, acute-on-chronic liver failure, and even mortality. It is of paramount importance to identify various factors which are associated with unfavorable outcomes for prognostication and making informed management strategy. Many factors have been evaluated in different studies in patients with underlying CLD. Some of these factors include the severity of underlying chronic liver disease, comorbid conditions, age, and severity of COVID-19. Overall, the outcomes are not fav-orable in patients with cirrhosis as evidenced by data from various studies. The main purpose of this review is to identify the predictors of adverse clinical outcomes including mortality in patients with CLD for risk stratification, prognostication, and appropriate clinical management.

Twice evasions of Omicron variants explain the temporal patterns in six Asian and Oceanic countries

BACKGROUND

The ongoing coronavirus 2019 (COVID-19) pandemic has emerged and caused multiple pandemic waves in the following six countries: India, Indonesia, Nepal, Malaysia, Bangladesh and Myanmar. Some of the countries have been much less studied in this devastating pandemic. This study aims to assess the impact of the Omicron variant in these six countries and estimate the infection fatality rate (IFR) and the reproduction number [Formula: see text] in these six South Asia, Southeast Asia and Oceania countries.

METHODS

We propose a Susceptible-Vaccinated-Exposed-Infectious-Hospitalized-Death-Recovered model with a time-varying transmission rate [Formula: see text] to fit the multiple waves of the COVID-19 pandemic and to estimate the IFR and [Formula: see text] in the aforementioned six countries. The level of immune evasion and the intrinsic transmissibility advantage of the Omicron variant are also considered in this model.

RESULTS

We fit our model to the reported deaths well. We estimate the IFR (in the range of 0.016 to 0.136%) and the reproduction number [Formula: see text] (in the range of 0 to 9) in the six countries. Multiple pandemic waves in each country were observed in our simulation results.

CONCLUSIONS

The invasion of the Omicron variant caused the new pandemic waves in the six countries. The higher [Formula: see text] suggests the intrinsic transmissibility advantage of the Omicron variant. Our model simulation forecast implies that the Omicron pandemic wave may be mitigated due to the increasing immunized population and vaccine coverage.

Effects of vaccination on mitigating COVID-19 outbreaks: a conceptual modeling approach

This paper is devoted to investigating the impact of vaccination on mitigating COVID-19 outbreaks. In this work, we propose a compartmental epidemic ordinary differential equation model, which extends the previous so-called SEIRD model ^[1,2,3,4] by incorporating the birth and death of the population, disease-induced mortality and waning immunity, and adding a vaccinated compartment to account for vaccination. Firstly, we perform a mathematical analysis for this model in a special case where the disease transmission is homogeneous and vaccination program is periodic in time. In particular, we define the basic reproduction number $ \mathcal{R}_0 $ for this system and establish a threshold type of result on the global dynamics in terms of $ \mathcal{R}_0 $. Secondly, we fit our model into multiple COVID-19 waves in four locations including Hong Kong, Singapore, Japan, and South Korea and then forecast the trend of COVID-19 by the end of 2022. Finally, we study the effects of vaccination again the ongoing pandemic by numerically computing the basic reproduction number $ \mathcal{R}_0 $ under different vaccination programs. Our findings indicate that the fourth dose among the high-risk group is likely needed by the end of the year.

Association of coagulation factors profile with clinical outcome in patient with COVID-19 and acute stroke: A second wave cohort study

INTRODUCTION

The second wave of COVID-19 in Indonesia occurred due to delta variant transmission with up to 2266 cases. This variant could cause higher rate of morbidities and mortalities. This study reported coagulation profile of COVID-19 patients with acute stroke and its association with patients' outcome.

METHOD

This is a cohort-retrospective study conducted during the second wave of COVID-19, June-August 2021 in Cipto Mangunkusumo General Hospital. Inclusion criteria were adult patients with confirmed COVID-19 and diagnosed with acute stroke confirmed by radiological evidences. Exclusion criteria were COVID-19 patients with prior diagnosis of acute stroke. Coagulation factors were analyzed and presented with tables and graphs.

RESULTS

A total of 33 patients included in this study with majority experienced ischemic stroke (84.8%), followed by ischemic with haemorrhagic transformation (9.1%), and the rest with haemorrhagic stroke. The median of fibrinogen and D-dimer was 487.1(147-8,943)mg/dL and 2,110(250-35,200)ug/L respectively. Prothrombin time (PT) ratio was 0.95(0.82-1.3) and activated partial thromboplastin time (APTT) ratio was 1.01(0.64-2.72). On observation, 33.3% died during hospitalization, D-dimer value in these patients was significantly higher with 9,940ug/L compared to those who survived with 1,160ug/L(p = 0.009). The highest D-dimer value during hospitalization was also significantly higher with the median of 14,395ug/L compared to 3,740 ug/L (p = 0.014).

DISCUSSION

D-dimer value on initial assessment and its highest value during hospitalization were significantly higher in patient with poor outcome, showing that D-dimer can be one predictor of mortality in COVID-19 patients with acute stroke.

Global stability and optimal control for a COVID-19 model with vaccination and isolation delays

COVID-19 pandemic remains serious around the world and causes huge deaths and economic losses. To investigate the effect of vaccination and isolation delays on the transmission of COVID-19, we propose a mathematical model of COVID-19 transmission with vaccination and isolation delays. The basic reproduction number is computed, and the global dynamics of the model are proved. WhenR 0 < 1 , the disease-free equilibrium is globally asymptotically stable. The unique endemic equilibrium is globally asymptotically stable ifR 0 > 1 . Based on the public information, parameter values are estimated, and sensitivity analysis is carried out by the partial rank correlation coefficients (PRCCs) and the extended version of the Fourier amplitude sensitivity test (eFAST). Our results suggest that the isolation rates of asymptomatic and symptomatic infectious individuals have a significant impact on the transmission of COVID-19. When the COVID-19 is epidemic, the optimal control strategies of our model with vaccination and isolation delays are analyzed. Under the limited resource with constant and time-varying isolation rates, we find that the optimal isolation rates may minimize the cumulative number of infected individuals and the cost of disease control, and effectively contain the transmission of COVID-19. Our study may help public health to prevent and control the COVID-19 spread.

Modelling and stability analysis of ASFV with swill and the virus in the environment

African swine fever (ASF) is an acute, hemorrhagic and severe infectious disease caused by the African swine fever virus (ASFV), and leads to a serious threat to the pig industry in China. Yet the impact of the virus in the environment and contaminated swill on the ASFV transmission is unclear in China. Then we build the ASFV transmission model with the virus in the environment and swill. We compute the basic reproduction number, and prove that the disease-free equilibrium is globally asymptotically stable when $ R_0 < 1 $ and the unique endemic equilibrium is globally asymptotically stable when $ R_0 > 1 $. Using the public information, parameter values are evaluated. PRCCs and eFAST sensitivity analysis reveal that the release rate of ASFV from asymptomatic and symptomatic infectious pigs and the proportion of pig products from infectious pigs to swill have a significant impact on the ASFV transmission. Our findings suggest that the virus in the environment and contaminated swill contribute to the ASFV transmission. Our results may help animal health to prevent and control the ASFV transmission.

A simple model to estimate the transmissibility of the Beta, Delta, and Omicron variants of SARS-COV-2 in South Africa

The COVID-19 pandemic caused multiple waves of mortality in South Africa, where three genetic variants of SARS-COV-2 and their ancestral strain dominated consecutively. State-of-the-art mathematical modeling approach was used to estimate the time-varying transmissibility of SARS-COV-2 and the relative transmissibility of Beta, Delta, and Omicron variants. The transmissibility of the three variants were about 73%, 87%, and 276% higher than their preceding variants. To the best of our knowledge, our model is the first simple model that can simulate multiple mortality waves and three variants' replacements in South Africa. The transmissibility of the Omicron variant is substantially higher than that of previous variants.

Impact of low vaccine coverage on the resurgence of COVID-19 in Central and Eastern Europe

The coronavirus disease 2019 (COVID-19) pandemic has caused a tremendous global impact both socially and economically. The mechanisms behind the disparity in the severity, vaccine coverage, and variant replacement patterns across European countries are unclear. In this work, we aim to reveal the possible reasons via data visualization and model fitting. We developed a model with a vaccination component to simulate the mortality waves in these countries. Deaths averted by the vaccination campaign were estimated. Finally, we discuss the potential reasons behind the differences in vaccine coverage across European countries. Contemporary transportation and global trade bring significant convenience to our daily life but also facilitate the spread of the novel virus COVID-19 to anywhere globally within a short time. The observations and results in this work highlight the importance of the global campaign to mitigate the COVID-19 pandemic and future pandemics under the One Health approach.

•

We reveal disparity in COVID-19 vaccine coverage across European counties.

•

We reveal different patterns of COVID-19 variants across European countries.

•

Using a mathematical model, we calculate deaths averted by the vaccine in Europe.

•

We discuss the reasons behind the disparity in vaccine coverage in Europe.

Multiple COVID-19 Waves and Vaccination Effectiveness in the United States

(1) Background: The coronavirus 2019 (COVID-19) pandemic has caused multiple waves of cases and deaths in the United States (US). The wild strain, the Alpha variant (B.1.1.7) and the Delta variant (B.1.617.2) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were the principal culprits behind these waves. To mitigate the pandemic, the vaccination campaign was started in January 2021. While the vaccine efficacy is less than 1, breakthrough infections were reported. This work aims to examine the effects of the vaccination across 50 US states and the District of Columbia. (2) Methods: Based on the classic Susceptible-Exposed-Infectious-Recovered (SEIR) model, we add a delay class between infectious and death, a death class and a vaccinated class. We compare two special cases of our new model to simulate the effects of the vaccination. The first case expounds the vaccinated individuals with full protection or not, compared to the second case where all vaccinated individuals have the same level of protection. (3) Results: Through fitting the two approaches to reported COVID-19 deaths in all 50 US states and the District of Columbia, we found that these two approaches are equivalent. We calculate that the death toll could be 1.67-3.33 fold in most states if the vaccine was not available. The median and mean infection fatality ratio are estimated to be approximately 0.6 and 0.7%. (4) Conclusions: The two approaches we compared were equivalent in evaluating the effectiveness of the vaccination campaign in the US. In addition, the effect of the vaccination campaign was significant, with a large number of deaths averted.

Modelling COVID-19 vaccine breakthrough infections in highly vaccinated Israel-The effects of waning immunity and third vaccination dose

In August 2021, a major wave of the SARS-CoV-2 Delta variant erupted in the highly vaccinated population of Israel. The transmission advantage of the Delta variant enabled it to replace the Alpha variant in approximately two months. The outbreak led to an unexpectedly large proportion of breakthrough infections (BTI)-a phenomenon that received worldwide attention. Most of the Israeli population, especially those aged 60+, received their second dose of the vaccination four months before the invasion of the Delta variant. Hence, either the vaccine induced immunity dropped significantly or the Delta variant possesses immunity escaping abilities, or both. In this work, we model data obtained from the Israeli Ministry of Health, to help understand the epidemiological factors involved in the outbreak. We propose a mathematical model that captures a multitude of factors, including age structure, the time varying vaccine efficacy, time varying transmission rate, BTIs, reduced susceptibility and infectivity of vaccinated individuals, protection duration of the vaccine induced immunity, and the vaccine distribution. We fitted our model to COVID-19 cases among the vaccinated and unvaccinated, for <60 and 60+ age groups, and quantified the transmission rate, the vaccine efficacy over time and the impact of the third dose booster vaccine. The peak transmission rate of the Delta variant was found to be 2.14 times higher than that of the Alpha variant. The two-dose vaccine efficacy against infection dropped significantly from >90% to ~40% over 6 months. We further performed model simulations and quantified counterfactual scenarios examining what would happen if the booster had not been rolled out. We estimated that approximately 4.03 million infective cases (95%CI 3.19, 4.86) were prevented by vaccination overall, and 1.22 million infective cases (95%CI 0.89, 1.62) averted by the booster.