There are many COVID-19 vaccines currently available, however, Low- and middle-income countries (LMIC) still have large proportions of their populations unvaccinated. Decision-makers must decide how to effectively allocate available vaccines (e.g. boosters or primary series vaccination, which age groups to target) but LMIC often lack the resources to undergo quantitative analyses of vaccine allocation, resulting in ad-hoc policies. We developed Covid19Vaxplorer (https://covid19vaxplorer.fredhutch.org/), a free, user-friendly online tool that simulates region-specific COVID-19 epidemics in conjunction with vaccination with the purpose of providing public health officials worldwide with a tool for vaccine allocation planning and comparison. We developed an age-structured mathematical model of SARS-CoV-2 transmission and COVID-19 vaccination. The model considers vaccination with up to three different vaccine products, primary series and boosters. We simulated partial immunity derived from waning of natural infection and vaccination. The model is embedded in an online tool, Covid19Vaxplorer that was optimized for its ease of use. By prompting users to fill information through several windows to input local parameters (e.g. cumulative and current prevalence), epidemiological parameters (e.g basic reproduction number, current social distancing interventions), vaccine parameters (e.g. vaccine efficacy, duration of immunity) and vaccine allocation (both by age groups and by vaccination status). Covid19Vaxplorer connects the user to the mathematical model and simulates, in real time, region-specific epidemics. The tool then produces key outcomes including expected numbers of deaths, hospitalizations and cases, with the possibility of simulating several scenarios of vaccine allocation at once for a side-by-side comparison. We provide two usage examples of Covid19Vaxplorer for vaccine allocation in Haiti and Afghanistan, which had as of Spring 2023, 2% and 33% of their populations vaccinated, and show that for these particular examples, using available vaccine as primary series vaccinations prevents more deaths than using them as boosters. 

pdf/covid19vaxplorer-a-free-online-user-friendly-covid-19-220d7nmj81.pdf

Covid19Vaxplorer: A free, online, user-friendly COVID-19 vaccine allocation comparison tool

To better understand the impact of COVID-19 epidemic on routine vaccination, a cross-sectional study was carried out during non-pharmaceutical intervention period (2020) and mass COVID-19 vaccination period (2021) in Shaanxi province. Data on NIP (National Immunization Program, Free), nNIP (non-National Immunization Program, Paid), and COVID-19 vaccines inoculated and vaccination clinics were collected. At the beginning of the COVID-19 epidemic, the rate of routine vaccination clinics providing vaccination services was only 20.0%, rabies vaccination clinics was 89.9% and obstetric vaccination clinics was 95.9%. Compared with 2019 (as baseline), NIP vaccines inoculated in 2020 and 2021 decreased by 3.5% and 19.2%, respectively, on the contrary, nNIP vaccines increased by 25.8% and 34.7%, respectively. In 2021, 74.9% of vaccination clinics were involved in mass COVID-19 vaccination, and there was not a significant impact of mass COVID-19 vaccination on the number of other vaccines inoculated. Vaccination coverage of all NIP vaccines was not different from that in previous years. COVID-19 epidemic had a short-term impact on routine vaccination at beginning. The impact of mass COVID-19 vaccination on routine vaccination could even be ignored. The COVID-19 epidemic has once again made people realize the great harm of infectious diseases, and has improved everyone’s willingness to receive routine vaccines. 

Impact of the COVID-19 pandemic on routine vaccination services in Shaanxi province, Northwest China: Non-pharmaceutical intervention period and mass COVID-19 vaccination period

Accurate epidemic forecasting is critical for effective public health interventions. This study compares Bayesian and Frequentist estimation frameworks within deterministic compartmental epidemic models, focusing on nonlinear least squares (NLS) optimization versus Bayesian inference assuming a normal likelihood and using MCMC sampling in Stan . Rather than evaluating all methodological variants, we assess forecasting performance under a shared modeling structure and error assumption. The findings apply to specific implementations of both approaches. Performance is evaluated using simulated datasets (with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="script">R</mml:mi> </mml:mrow> <mml:mn>0</mml:mn> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>2</mml:mn> </mml:math> and 1.5) and historical outbreaks, including the 1918 influenza pandemic, the 1896–1897 Bombay plague epidemic, and the COVID-19 pandemic. Metrics include mean absolute error (MAE), root mean squared error (RMSE), weighted interval score (WIS), and 95% prediction interval coverage. Forecasting performance varies by epidemic phase and dataset; no method consistently dominates. The Frequentist method performs well at the peak in simulations and in the post-peak phases of real outbreaks but is less accurate pre-peak. Bayesian methods, especially those with uniform priors, offer higher predictive accuracy early in epidemics and stronger uncertainty quantification when data are sparse or noisy. Frequentist methods often yield more accurate point forecasts with lower MAE, RMSE, and WIS, though their interval estimates are less robust. We also discuss the influence of prior choice and the effects of longer forecasting horizons on convergence and computational efficiency. These findings provide practical guidance for selecting estimation strategies suited to epidemic phase and data quality, aiding forecast-based decision-making. 

pdf/comparative-study-of-bayesian-and-frequentist-methods-for-xm8vrl7u142t.pdf

Comparative study of Bayesian and frequentist methods for epidemic forecasting: Insights from simulated and historical data

Abstract The recently derived Hybrid-Incidence Susceptible-Transmissible-Removed (HI-STR) prototype is a deterministic compartment model for epidemics and an alternative to the Susceptible-Infected-Removed (SIR) model. The HI-STR predicts that pathogen transmission depends on host population characteristics including population size, population density and social behaviour common within that population. The HI-STR prototype is applied to the ancestral Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) to show that the original estimates of the Coronavirus Disease 2019 (COVID-19) basic reproduction number $\mathcal{R}_{0}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>R</mml:mi> <mml:mn>0</mml:mn> </mml:msub> </mml:math> for the United Kingdom (UK) could have been projected onto the individual states of the United States of America (USA) prior to being detected in the USA. The Imperial College London (ICL) group’s estimate of $\mathcal{R}_{0}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>R</mml:mi> <mml:mn>0</mml:mn> </mml:msub> </mml:math> for the UK is projected onto each USA state. The difference between these projections and the ICL’s estimates for USA states is either not statistically significant on the paired Student t -test or not epidemiologically significant. The SARS-CoV2 Delta variant’s $\mathcal{R}_{0}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>R</mml:mi> <mml:mn>0</mml:mn> </mml:msub> </mml:math> is also projected from the UK to the USA to prove that projection can be applied to a Variant of Concern (VOC). Projection provides both a localised baseline for evaluating the implementation of an intervention policy and a mechanism for anticipating the impact of a VOC before local manifestation. 

Reproduction number projection for the COVID-19 pandemic

Parrondo’s paradox was introduced by Juan Parrondo in 1996. In game theory, this paradox is described as: A combination of losing strategies becomes a winning strategy. At first glance, this paradox is quite surprising, but we can easily explain it by using simulations and mathematical arguments. Indeed, we first consider some examples with the Parrondo’s paradox and, using the software R, we simulate one of them, the coin tossing. Actually, we see that specific combinations of losing games become a winning game. Moreover, even a random combination of these two losing games leads to a winning game. Later, we introduce the major definitions and theorems over Markov chains to study our Parrondo’s paradox applied to the coin tossing problem. In particular, we represent our Parrondo’s game as a Markov chain and we find its stationary distribution. In that way, we exhibit that our combination of two losing games is truly a winning combination. We also deliberate possible applications of the paradox in some fields such as ecology, biology, finance or reliability theory.

/pdf/markov-chains-applied-to-parrondo-s-paradox-the-coin-tossing-1ehmfgs6.pdf

Markov chains applied to Parrondo's paradox: The coin tossing problem

Real-time forecasting of COVID-19 spread according to protective behavior and vaccination: autoregressive integrated moving average models

Objective: To understand the epidemiological characteristics of COVID-19 epidemic in Ejina banner, Inner Mongolia, in October 2021 and provide evidence for the improvement of COVID-19 prevention and control. Methods: The information about the time, area and population distributions of COVID-19 cases in Ejina before November 13, 2021 and the gene sequencing result of the isolates were collected for a statistical descriptive analysis. Results: The first COVID-19 case in Ejina occurred on 7 October, 2021. A total of 164 COVID-19 cases were reported from October 19 to November 12. Most cases were distributed in 6 communities in Darahub (156 cases, 95.12%). The result of full gene sequencing of the isolates indicted that the pathogen was Delta variant (B.1.617.2). The male to female ratio of the cases was 1.3∶1. The age of cases ranged from 1 to 85 years, and the cases aged 20-59 years accounted for 78.66%. The main clinical symptoms were sore throat (91 cases, 91.92%), cough (49 cases, 49.49%) and fever (23 cases, 23.23%). Most cases were ordinary ones (81 cases, 49.39%) and mild ones (68 cases, 41.46%). The cases were mainly detected at the isolation points (84 cases, 51.22%) and through population based nucleic acid testing (62 cases, 37.80%). The basic reproduction number (R0) of COVID-19 was 5.3, the average incubation period was 3.9 days. The local government rapidly started Ⅳ level emergency response and conducted 10 rounds of nucleic acid tests. The transferring of travelers reduced the risk for the further spread of COVID-19 in Ejina. Conclusions: The epidemic of COVID-19 in Ejina characterized by strong transmission, short incubation period, herd susceptibility and case clustering. Delta variant (B.1.617.2) was the pathogen, which might be imported from Zeke port. Comprehensive prevention and control measures, such as closed-loop management and vaccination, should be continued. The successful transferring of the patients and travelers provided evidence for the effective and precise prevention and control of COVID-19 in a routine manner.

[Epidemiological characteristics of COVID-19 epidemic in Ejina banner, Inner Mongolia Autonomous Region, October 2021].

Background Taiwan was a coronavirus disease 2019 (COVID-19) outlier, with an extraordinarily long transmission-free record: 253 days without locally transmitted infections while the rest of the world battled wave after wave of infection. The appearance of the alpha variant in May 2021, closely followed by the delta variant, disrupted this transmission-free streak. However, despite low vaccination coverage (<1%), outbreaks were well-controlled. Methods This study analyzed the time to border closure and conducted one-sample t test to compare between Taiwan and Non-Taiwan countries prior to vaccine introduction. The study also collected case data to observe the dynamics of omicron transmission. Time-varying reproduction number,Rt, was calculated and was used to reflect infection impact at specified time points and model trends of future incidence. Results The study analyzed and compare the time to border closure in Taiwan and non-Taiwan countries. The mean times to any border closure from the first domestic case within each country were -21 and 5.98 days, respectively (P < .0001). The Taiwanese government invested in quick and effective contact tracing with a precise quarantine strategy in lieu of a strict lockdown. Residents followed recommendations based on self-discipline and unity. The self-discipline in action is evidenced in Google mobility reports. The central and local governments worked together to enact non-pharmaceutical interventions (NPIs), including universal masking, social distancing, limited unnecessary gatherings, systematic contact tracing, and enhanced quarantine measures. The people cooperated actively with pandemic-prevention regulations, including vaccination and preventive NPIs. Conclusions This article describes four key factors underlying Taiwan’s success in controlling COVID-19 transmission: quick responses; effective control measures with new technologies and rolling knowledge updates; unity and cooperation among Taiwanese government agencies, private companies and organizations, and individual citizens; and Taiwanese self-discipline.

Suppression of the alpha, delta, and omicron variants of SARS-Cov-2 in Taiwan

This modeling study considers different screening strategies, contact tracing, and the severity of novel epidemic outbreaks for various population sizes, providing insight into multinational containment effectiveness of emerging infectious diseases, prior to vaccines development. During the period of the ancestral SARS-Cov-2 virus, contact tracing alone is insufficient to achieve outbreak control. Although universal testing is proposed in multiple nations, its effectiveness accompanied by other measures is rarely examined. Our research investigates the necessity of universal testing when contact tracing and symptomatic screening measures are implemented. We used a stochastic transmission model to simulate COVID-19 transmission, evaluating containment strategies via contact tracing, one-time high risk symptomatic testing, and universal testing. Despite universal testing having the potential to identify subclinical cases, which is crucial for non-pharmaceutical interventions, our model suggests that universal testing only reduces the total number of cases by 0.0009% for countries with low COVID-19 prevalence and 0.025% for countries with high COVID-19 prevalence when rigorous contact tracing and symptomatic screening are also implemented. These findings highlight the effectiveness of testing strategies and contact tracing in reducing COVID-19 cases by identifying subclinical cases.

/pdf/contribution-of-testing-strategies-and-contact-tracing-16z3u802.pdf

Contribution of Testing Strategies and Contact Tracing towards COVID-19 Outbreaks Control: A Mathematical Modeling Study

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Papers

Real-time forecasting of COVID-19 spread according to protective behavior and vaccination: autoregressive integrated moving average models

[Epidemiological characteristics of COVID-19 epidemic in Ejina banner, Inner Mongolia Autonomous Region, October 2021].

Suppression of the alpha, delta, and omicron variants of SARS-Cov-2 in Taiwan

Contribution of Testing Strategies and Contact Tracing towards COVID-19 Outbreaks Control: A Mathematical Modeling Study