Tracking R of COVID-19: A new real-time estimation using the Kalman filter

Diphtheria transmission prediction by extended Kalman filter

Diphtheria transmission in West Java becomes our concern in this paper. The findings of this article are implementation of isolation and estimation technique of parameters using extended Kalman filter on the model of diphtheria transmission. From the eigenvalues of next generation matrix, the basic reproduction number is obtained. Then, the implementation of using extended Kalman filter provides the trend of the basic reproduction number of the diphtheria model with several cases: DPT only, Booster only, and a combination of DPT and booster. Based on the values of RMSE, NRMSE, and MAPE, the Extended Kalman Filter method provides significant results in estimating the basic reproduction number on actual data of diphtheria cases in West Java. We also studied the quarantined population in this article, because the rate of isolation has a significant impact on the profile of the susceptible, infected, quarantined, and recovered populations. Based on the results obtained, DPT only gives the smallest number when compared to Booster only and the combination of DPT and Booster. This is likely when DPT only is given, the body forms an immune system so giving Booster only does not provide significant results in reducing the level of effectiveness of diphtheria transmission (reducing the basic reproduction number).•This paper purposes to get the prediction of diphtheria transmission by using Extended Kalman Filter.•The basic reproduction number is also studied for the DPT only, booster only, and the combinations of DPT and booster.•The Extended Kalman Filter shows the good performance based on the RMSE, NRMSE, and MAPE.

Parameter estimation of stochastic SEIR epidemic model using particle MCMC

This paper presents a novel Bayesian inference algorithm for estimating unknown parameters in a stochastic susceptible-exposed-infected-recovered (SEIR) model, aiming to predict the extinction and persistence of infectious diseases. The posterior distribution is constructed using Gaussian processes, and sampling is generated via particle Markov Chain Monte Carlo. A key feature of our method is its gradient-based proposal mechanism, which enhances efficiency compared to traditional random-walk proposals. The algorithm can converge to the stationary distribution within a reasonable time frame, even when handling multiple parameters. Numerical simulations illustrate the effectiveness of our algorithm in parameter estimation. Additionally, several useful theoretical properties of the stochastic SEIR model are discussed. As an application example, the algorithm has been applied to estimate parameters from COVID-19 data in Iceland.

Machine learning models to predict the COVID-19 reproduction rate: combining non-pharmaceutical interventions with sociodemographic and cultural characteristics

Since the beginning of the COVID-19 pandemic, countries worldwide have implemented a set of Non-Pharmaceutical Interventions (NPIs) to prevent the dissemination of the pandemic. Few studies applied machine learning models to compare the use of NPIs, socioeconomic and demographic characteristics, and cultural dimensions in predicting the reproduction rate Rt. We adopted the CRISP-DM methodology using as data sources the "Our World in Data COVID-19," the "Oxford COVID-19 Government Response Tracker" and the Hofstede Insights data. We analyzed the impact that Hofstede's cultural dimensions, the implementation of various degrees of restriction of NPIs and the sociodemographic variables may have in the reproduction rate by applying machine learning models to understand whether cultural characteristics are useful information to improve reproduction rate predictions. We included data from 101 countries to train several machine learning models to compare the results between the models with and without Hofstede's cultural dimensions. Our results show the use of cultural dimensions helps to improve the models, and that the ones that obtained a better prediction of the Rt were the ensemble models, especially the Random Forest.

Kalman Filter-Based Epidemiological Model for Post-COVID-19 Era Surveillance and Prediction

In the post-COVID-19 era, the dynamic spread of COVID-19 poses new challenges to epidemiological modelling, particularly due to the absence of large-scale screening and the growing complexity introduced by immune failure and reinfections. This paper proposes an AEIHD (antibody-acquired, exposed, infected, hospitalised, and deceased) model to analyse and predict COVID-19 transmission dynamics in the post-COVID-19 era. This model removes the susceptible compartment and combines the recovered and vaccinated compartments into an "antibody-acquired" compartment. It also introduces a new hospitalised compartment to monitor severe cases. The model incorporates an antibody-acquired infection rate to account for immune failure. The Extended Kalman Filter based on the AEIHD model is proposed for real-time state and parameter estimation, overcoming the limitations of fixed-parameter approaches and enhancing adaptability to nonlinear dynamics. Simulation studies based on reported data from Australia validate the AEIHD model, demonstrating its capability to accurately capture COVID-19 transmission dynamics with limited statistical information. The proposed approach addresses the key limitations of traditional SIR and SEIR models by integrating hospitalisation data and time-varying parameters, offering a robust framework for monitoring and predicting epidemic behaviours in the post-COVID-19 era. It also provides a valuable tool for public health decision-making and resource allocation to handle rapidly evolving epidemiology.

Estimating the causal impact of non-pharmaceutical interventions on COVID-19 spread in seven EU countries via machine learning

During the COVID-19 pandemic, Non-Pharmaceutical Interventions (NPIs) were imposed all over Europe with the intent to reduce infection spread. However, reports on the effectiveness of those measures across different European countries are inconclusive up to now. Moreover, attempts to predict the effect of NPIs in a prospective and dynamical manner with the aim to support decision makers in future global health emergencies are largely lacking. Here, we explore causal machine learning to isolate causal effects of NPIs in observational public health data from seven EU countries, taking into account specific challenges like their sequential nature, effect heterogeneity, time-dependent confounding and lack of robustness due to violated assumptions. In a pseudo-prospective scenario planning analysis, we investigate which recommendations our model would have made during the second wave of the pandemic in Germany, demonstrating its capacity to generalize to the near future and identifying effective NPIs. In retrospect, our approach indicates that a wide range of response measures curbed COVID-19 across countries, especially in the early phases of the pandemic. Interestingly, this includes controversial interventions like strict school and border closures, but also recommendation-based policies in Sweden. Finally, we discuss important data- and modeling-related considerations that may optimize causal effect estimation in future pandemics.

Optimal COVID-19 vaccine prioritization by age depends critically on inter-group contacts and vaccination rates

The limited availability of COVID-19 vaccines has prompted extensive research on optimal vaccination strategies. Previous studies have considered various non-pharmaceutical interventions, vaccine efficacy and distribution strategies. In this work, we address the combined effects of inter-group contacts and vaccination rates under contact reduction, analysing the Spanish population's demographic and age group contact patterns and incorporating reinfection dynamics. We conduct an exhaustive analysis, evaluating 362 880 permutations of nine age groups across six vaccination rates and two distinct, empirically quantified scenarios for social contacts. Our results show that at intermediate-to-high vaccination rates with unrestricted social contacts, optimal age-based vaccination strategies only slightly deviate from older-to-younger prioritization, yielding marginal reductions in deaths and infections. However, when significant reductions in social contacts are enforced-similar to the lockdowns in 2020-there are substantial improvements, particularly at moderate vaccination rates. These restrictions lead to a transition where infection propagation is halted, a scenario that became achievable during the pandemic with the observed vaccination rates. Our findings emphasize the importance of combining appropriate social contact reductions with vaccination to optimize age-based vaccination strategies, underscoring the complex, nonlinear dynamics involved in pandemic dynamics and the necessity for tailored context-specific interventions.

Long-term trends in cycle threshold values: a comprehensive analysis of COVID-19 dynamics, viral load, and reproduction number in South Korea

Background

With the emergence of COVID-19 cases, governments quickly responded with aggressive testing, contact tracing, isolation and quarantine measures. South Korea's testing strategy primarily relied on real-time reverse-transcriptase polymerase chain reaction (real-time RT-PCR), focusing on cycle threshold (Ct) values, indicative of viral load, to determine COVID-19 positivity. This study examined the long-term time series distribution of Ct values measured in the same laboratory using a nationally standardized testing type and sampling method in South Korea. It aimed to link Ct values, new COVID-19 cases, and the reproduction number (Rt), setting the stage for using Ct values effectively.

Methods

This study analyzed nationally collected 296,347 samples Ct values from February 2020 to January 2022 and examined their associations with the number of new cases and Rt trends. The data were categorized into four COVID-19 periods for in-depth analysis. Statistical methods included time series trend analysis, local regression for smoothing, linear regression for association analysis, and calculation of correlation coefficients.

Results

The median Ct values across four COVID-19 periods decreased gradually from 31.71 in the initial period to 21.27 in the fourth period, indicating higher viral load. The comparison of trends between Ct values and the number of new cases revealed that the decline in Ct values preceded the surge in new cases, particularly evident during the initial stages when new cases did not undergo a significant increase. Also, during variant emergence and vaccination rollout, marked shifts in Ct values were observed. Results from linear regression analysis revealed a significant negative relationship between Ct values and new cases (β = -0.33, p < 0.001, R 2 = 0.67). This implies that as Ct values decrease, new case numbers increase.

Conclusion

This study demonstrates the potential of Ct values as early indicators for predicting confirmed COVID-19 cases during the initial stages of the epidemic and suggests their relevance in large-scale epidemic monitoring, even when case numbers are similar.

Bayesian model selection for COVID-19 pandemic state estimation using extended Kalman filters: Case study for Saudi Arabia

Quantifying the uncertainty in data-driven mechanistic models is fundamental in public health applications. COVID-19 is a complex disease that had a significant impact on global health and economies. Several mathematical models were used to understand the complexity of the transmission dynamics under different hypotheses to support the decision-making for disease management. This paper highlights various scenarios of a 6D epidemiological model known as SEIQRD (Susceptible-Exposed-Infected-Quarantined-Recovered-Deceased) to evaluate its effectiveness in prediction and state estimation during the spread of COVID-19 pandemic. Then we investigate the suitability of the classical 4D epidemiological model known as SIRD (Susceptible-Infected-Recovered-Deceased) in the long-term behaviour in order to make a comparison between these models. The primary aim of this paper is to establish a foundational basis for the validity and epidemiological model comparisons in long-term behaviour which may help identify the degree of model complexity that is required based on two approaches viz. the Bayesian inference employing the nested sampling algorithm and recursive state estimation utilizing the Extended Kalman Filter (EKF). Our approach acknowledges the potential imperfections and uncertainties inherent in compartmental epidemiological models. By integrating our proposed methodology, these models can consistently generate predictions closely aligned with the observed data on active cases and deaths. This framework, implemented within the EKF algorithm, offers a robust tool for addressing future, unknown pandemics. Moreover, we present a systematic methodology for time-varying parameter estimation along with uncertainty quantification using Saudi Arabia COVID-19 data and obtain the credible confidence intervals of the epidemiological nonlinear dynamical system model parameters.

Estimating time-varying epidemiological parameters and underreporting of Covid-19 cases in Brazil using a mathematical model with fuzzy transitions between epidemic periods

Our study conducts a comprehensive analysis of the Covid-19 pandemic in Brazil, spanning five waves over three years. We employed a novel Susceptible-Infected-Recovered-Dead-Susceptible (SIRDS) model with a fuzzy transition between epidemic periods to estimate time-varying parameters and evaluate case underreporting. The initial basic reproduction number (R0) is identified at 2.44 (95% Confidence Interval (CI): 2.42-2.46), decreasing to 1.00 (95% CI: 0.99-1.01) during the first wave. The model estimates an underreporting factor of 12.9 (95% CI: 12.5-13.2) more infections than officially reported by Brazilian health authorities, with an increasing factor of 5.8 (95% CI: 5.2-6.4), 12.9 (95% CI: 12.5-13.3), and 16.8 (95% CI: 15.8-17.5) in 2020, 2021, and 2022 respectively. Additionally, the Infection Fatality Rate (IFR) is initially 0.88% (95% CI: 0.81%-0.94%) during the initial phase but consistently reduces across subsequent outbreaks, reaching its lowest value of 0.018% (95% CI: 0.011-0.033) in the last outbreak. Regarding the immunity period, the observed uncertainty and low sensitivity indicate that inferring this parameter is particularly challenging. Brazil successfully reduced R0 during the first wave, coinciding with decreased human mobility. Ineffective public health measures during the second wave resulted in the highest mortality rates within the studied period. We attribute lower mortality rates in 2022 to increased vaccination coverage and the lower lethality of the Omicron variant. We demonstrate the model generalization by its application to other countries. Comparative analyses with serological research further validate the accuracy of the model. In forecasting analysis, our model provides reasonable outbreak predictions. In conclusion, our study provides a nuanced understanding of the Covid-19 pandemic in Brazil, employing a novel epidemiological model. The findings contribute to the broader discourse on pandemic dynamics, underreporting, and the effectiveness of health interventions.

The interaction of disease transmission, mortality, and economic output over the first 2 years of the COVID-19 pandemic

BACKGROUND

The COVID-19 pandemic has caused over 7.02 million deaths as of January 2024 and profoundly affected most countries' Gross Domestic Product (GDP). Here, we study the interaction of SARS-CoV-2 transmission, mortality, and economic output between January 2020 and December 2022 across 25 European countries.

METHODS

We use a Bayesian mixed effects model with auto-regressive terms to estimate the temporal relationships between disease transmission, excess deaths, changes in economic output, transit mobility and non-pharmaceutical interventions (NPIs) across countries.

RESULTS

Disease transmission intensity (logRt) decreases GDP and increases excess deaths, where the latter association is longer-lasting. Changes in GDP as well as prior week transmission intensity are both negatively associated with each other (-0.241, 95% CrI: -0.295 - -0.189). We find evidence of risk-averse behaviour, as changes in transit and prior week transmission intensity are negatively associated (-0.055, 95% CrI: -0.074 to -0.036). Our results highlight a complex cost-benefit trade-off from individual NPIs. For example, banning international travel is associated with both increases in GDP (0.014, 0.002-0.025) and decreases in excess deaths (-0.014, 95% CrI: -0.028 - -0.001). Country-specific random effects, such as the poverty rate, are positively associated with excess deaths while the UN government effectiveness index is negatively associated with excess deaths.

INTERPRETATION

The interplay between transmission intensity, excess deaths, population mobility and economic output is highly complex, and none of these factors can be considered in isolation. Our results reinforce the intuitive idea that significant economic activity arises from diverse person-to-person interactions. Our analysis quantifies and highlights that the impact of disease on a given country is complex and multifaceted. Long-term economic impairments are not fully captured by our model, as well as long-term disease effects (Long COVID).

Using a population-based Kalman estimator to model the COVID-19 epidemic in France: estimating associations between disease transmission and non-pharmaceutical interventions

In response to the COVID-19 pandemic caused by SARS-CoV-2, governments have adopted a wide range of non-pharmaceutical interventions (NPI). These include stringent measures such as strict lockdowns, closing schools, bars and restaurants, curfews, and barrier gestures such as mask-wearing and social distancing. Deciphering the effectiveness of each NPI is critical to responding to future waves and outbreaks. To this end, we first develop a dynamic model of the French COVID-19 epidemics over a one-year period. We rely on a global extended Susceptible-Infectious-Recovered (SIR) mechanistic model of infection that includes a dynamic transmission rate over time. Multilevel data across French regions are integrated using random effects on the parameters of the mechanistic model, boosting statistical power by multiplying integrated observation series. We estimate the parameters using a new population-based statistical approach based on a Kalman filter, used for the first time in analysing real-world data. We then fit the estimated time-varying transmission rate using a regression model that depends on the NPIs while accounting for vaccination coverage, the occurrence of variants of concern (VoC), and seasonal weather conditions. We show that all NPIs considered have an independent significant association with transmission rates. In addition, we show a strong association between weather conditions that reduces transmission in summer, and we also estimate increased transmissibility of VoC.

Effects of Face Mask Mandates on COVID-19 Transmission in 51 Countries: Retrospective Event Study

BACKGROUND

The question of the utility of face masks in preventing acute respiratory infections has received renewed attention during the COVID-19 pandemic. However, given the inconclusive evidence from existing randomized controlled trials, evidence based on real-world data with high external validity is missing.

OBJECTIVE

To add real-world evidence, this study aims to examine whether mask mandates in 51 countries and mask recommendations in 10 countries increased self-reported face mask use and reduced SARS-CoV-2 reproduction numbers and COVID-19 case growth rates.

METHODS

We applied an event study approach to data pooled from four sources: (1) country-level information on self-reported mask use was obtained from the COVID-19 Trends and Impact Survey, (2) data from the Oxford COVID-19 Government Response Tracker provided information on face mask mandates and recommendations and any other nonpharmacological interventions implemented, (3) mobility indicators from Google's Community Mobility Reports were also included, and (4) SARS-CoV-2 reproduction numbers and COVID-19 case growth rates were retrieved from the Our World in Data-COVID-19 data set.

RESULTS

Mandates increased mask use by 8.81 percentage points (P=.006) on average, and SARS-CoV-2 reproduction numbers declined on average by -0.31 units (P=.008). Although no significant average effect of mask mandates was observed for growth rates of COVID-19 cases (-0.98 percentage points; P=.56), the results indicate incremental effects on days 26 (-1.76 percentage points; P=.04), 27 (-1.89 percentage points; P=.05), 29 (-1.78 percentage points; P=.04), and 30 (-2.14 percentage points; P=.02) after mandate implementation. For self-reported face mask use and reproduction numbers, incremental effects are seen 6 and 13 days after mandate implementation. Both incremental effects persist for >30 days. Furthermore, mask recommendations increased self-reported mask use on average (5.84 percentage points; P<.001). However, there were no effects of recommendations on SARS-CoV-2 reproduction numbers or COVID-19 case growth rates (-0.06 units; P=.70 and -2.45 percentage points; P=.59). Single incremental effects on self-reported mask use were observed on days 11 (3.96 percentage points; P=.04), 13 (3.77 percentage points; P=.04) and 25 to 27 (4.20 percentage points; P=.048 and 5.91 percentage points; P=.01) after recommendation. Recommendations also affected reproduction numbers on days 0 (-0.07 units; P=.03) and 1 (-0.07 units; P=.03) and between days 21 (-0.09 units; P=.04) and 28 (-0.11 units; P=.05) and case growth rates between days 1 and 4 (-1.60 percentage points; P=.03 and -2.19 percentage points; P=.03) and on day 23 (-2.83 percentage points; P=.05) after publication.

CONCLUSIONS

Contrary to recommendations, mask mandates can be used as an effective measure to reduce SARS-CoV-2 reproduction numbers. However, mandates alone are not sufficient to reduce growth rates of COVID-19 cases. Our study adds external validity to the existing randomized controlled trials on the effectiveness of face masks to reduce the spread of SARS-CoV-2.

On the impact of mass screening for SARS-CoV-2 through self-testing in Greece

Background

Screening programs that pre-emptively and routinely test population groups for disease at a massive scale were first implemented during the COVID-19 pandemic in a handful of countries. One of these countries was Greece, which implemented a mass self-testing program during 2021. In contrast to most other non-pharmaceutical interventions (NPIs), mass self-testing programs are particularly attractive for their relatively small financial and social burden, and it is therefore important to understand their effectiveness to inform policy makers and public health officials responding to future pandemics. This study aimed to estimate the number of deaths and hospitalizations averted by the program implemented in Greece and evaluate the impact of several operational decisions.

Methods

Granular data from the mass self-testing program deployed by the Greek government between April and December 2021 were obtained. The data were used to fit a novel compartmental model that was developed to describe the dynamics of the COVID-19 pandemic in Greece in the presence of self-testing. The fitted model provided estimates on the effectiveness of the program in averting deaths and hospitalizations. Sensitivity analyses were used to evaluate the impact of operational decisions, including the scale of the program, targeting of sub-populations, and sensitivity (i.e., true positive rate) of tests.

Results

Conservative estimates show that the program reduced the reproduction number by 4%, hospitalizations by 25%, and deaths by 20%, translating into approximately 20,000 averted hospitalizations and 2,000 averted deaths in Greece between April and December 2021.

Conclusion

Mass self-testing programs are efficient NPIs with minimal social and financial burden; therefore, they are invaluable tools to be considered in pandemic preparedness and response.

Estimating hidden relationships in dynamical systems: Discovering drivers of infection rates of COVID-19

Discovering causal influences among internal variables is a fundamental goal of complex systems research. This paper presents a framework for uncovering hidden relationships from limited time-series data by combining methods from nonlinear estimation and information theory. The approach is based on two sequential steps: first, we reconstruct a more complete state of the underlying dynamical system, and second, we calculate mutual information between pairs of internal state variables to detail causal dependencies. Equipped with time-series data related to the spread of COVID-19 from the past three years, we apply this approach to identify the drivers of falling and rising infections during the three main waves of infection in the Chicago metropolitan region. The unscented Kalman filter nonlinear estimation algorithm is implemented on an established epidemiological model of COVID-19, which we refine to include isolation, masking, loss of immunity, and stochastic transition rates. Through the systematic study of mutual information between infection rate and various stochastic parameters, we find that increased mobility, decreased mask use, and loss of immunity post sickness played a key role in rising infections, while falling infections were controlled by masking and isolation.

Revisiting the complex time-varying effect of non-pharmaceutical interventions on COVID-19 transmission in the United States

Introduction

Although the global COVID-19 emergency ended, the real-world effects of multiple non-pharmaceutical interventions (NPIs) and the relative contribution of individual NPIs over time were poorly understood, limiting the mitigation of future potential epidemics.

Methods

Based on four large-scale datasets including epidemic parameters, virus variants, vaccines, and meteorological factors across 51 states in the United States from August 2020 to July 2022, we established a Bayesian hierarchical model with a spike-and-slab prior to assessing the time-varying effect of NPIs and vaccination on mitigating COVID-19 transmission and identifying important NPIs in the context of different variants pandemic.

Results

We found that (i) the empirical reduction in reproduction number attributable to integrated NPIs was 52.0% (95%CI: 44.4, 58.5%) by August and September 2020, whereas the reduction continuously decreased due to the relaxation of NPIs in following months; (ii) international travel restrictions, stay-at-home requirements, and restrictions on gathering size were important NPIs with the relative contribution higher than 12.5%; (iii) vaccination alone could not mitigate transmission when the fully vaccination coverage was less than 60%, but it could effectively synergize with NPIs; (iv) even with fully vaccination coverage >60%, combined use of NPIs and vaccination failed to reduce the reproduction number below 1 in many states by February 2022 because of elimination of above NPIs, following with a resurgence of COVID-19 after March 2022.

Conclusion

Our results suggest that NPIs and vaccination had a high synergy effect and eliminating NPIs should consider their relative effectiveness, vaccination coverage, and emerging variants.

COVID-19 pandemic waves: Identification and interpretation of global data

The mention of the COVID-19 waves is as prevalent as the pandemic itself. Identifying the beginning and end of the wave is critical to evaluating the impact of various COVID-19 variants and the different pharmaceutical and non-pharmaceutical (including economic, health and social, etc.) interventions. We demonstrate a scientifically robust method to identify COVID-19 waves and the breaking points at which they begin and end from January 2020 to June 2021. Employing the Break Least Square method, we determine the significance of COVID-19 waves for global-, regional-, and country-level data. The results show that the method works efficiently in detecting different breaking points. Identifying these breaking points is critical for evaluating the impact of the economic, health, social and other welfare interventions implemented during the pandemic crisis. Employing our method with high frequency data effectively determines the start and end points of the COVID-19 wave(s). Identifying waves at the country level is more relevant than at the global or regional levels. Our research results evidenced that the COVID-19 wave takes about 48 days on average to subside once it begins, irrespective of the circumstances.

Comparing the Performance of Three Computational Methods for Estimating the Effective Reproduction Number

The effective reproduction number ( R t ) is one of the most important epidemiological parameters, providing suggestions for monitoring the development trend of diseases and also for adjusting the prevention and control policies. However, a few studies have focused on the performance of some common computational methods for Rt. The purpose of this article is to compare the performance of three computational methods for Rt: the time-dependent (TD) method, the new time-varying (NT) method, and the sequential Bayesian (SB) method. Four evaluation methods-accuracy, correlation coefficient, similarity based on trend, and dynamic time warping distance-were used to compare the effectiveness of three computational methods for Rt under different time lags and time windows. The results showed that the NT method was a better choice for real-time monitoring and analysis of the epidemic in the middle and late stages of the infectious disease. The TD method could reflect the change of the number of cases stably and accurately, and was more suitable for monitoring the change of Rt during the whole process of the epidemic outbreak. When the data were relatively stable, the SB method could also provide a reliable estimate for Rt, while the error would increase when the fluctuation in the number of cases increased. The results would provide suggestions for selecting appropriate Rt estimation methods and making policy adjustments more timely and effectively according to the change of Rt.

Systems Biology Approaches to Understanding COVID-19 Spread in the Population

In essence, the COVID-19 pandemic can be regarded as a systems biology problem, with the entire world as the system, and the human population as the element transitioning from one state to another with certain transition rates. While capturing all the relevant features of such a complex system is hardly possible, compartmental epidemiological models can be used as an appropriate simplification to model the system's dynamics and infer its important characteristics, such as basic and effective reproductive numbers of the virus. These measures can later be used as response variables in feature selection methods to uncover the main factors contributing to disease transmissibility. We here demonstrate that a combination of dynamic modeling and machine learning approaches can represent a powerful tool in understanding the spread, not only of COVID-19, but of any infectious disease of epidemiological proportions.

Recommended distances for physical distancing during COVID-19 pandemics reveal cultural connections between countries

During COVID-19 pandemic several public health measures were implemented by diverse countries to reduce the risk of COVID-19, including social distancing. Here we collected the minimal distance recommended by each country for physical distancing at the onset of the pandemic and aimed to examine whether it had an impact on the outbreak dynamics and how this specific value was chosen. Despite an absence of data on SARS-CoV-2 viral transmission at the beginning of the pandemic, we found that most countries recommended physical distancing with a precise minimal distance, between one meter/three feet and two meters/six feet. 45% of the countries advised one meter/three feet and 49% advised a higher minimal distance. The recommended minimal distance did not show a clear correlation with reproduction rate nor with the number of new cases per million, suggesting that the overall COVID-19 dynamics in each country depended on multiple interacting factors. Interestingly, the recommended minimal distance correlated with several cultural parameters: it was higher in countries with larger interpersonal distance between two interacting individuals in non-epidemic conditions, and it correlated with civil law systems, and with currency. This suggests that countries which share common conceptions such as civil law systems and currency unions tend to adopt the same public health measures.

State Capacity and COVID-19: Targeted versus Population-Wide Restrictions

CONTEXT

During the COVID-19 pandemic, governments varied in their implementation of social distancing rules. Some governments were able to target their social distancing requirements toward specific segments of the population, whereas others had to resort to more indiscriminate applications. This article will argue that state capacity crucially affected the manner in which social distancing rules were applied.

METHODS

Using data from the Oxford COVID-19 Government Response Tracker, the author performed a series of ordered logistic regressions to examine whether state capacity increased the likelihood of more targeted applications of each social distancing rule.

FINDINGS

Given the same level of infectivity, more capable states were indeed more likely to resort to targeted applications of each social distancing restriction. Interestingly, the size of state capacity's effect varied by the type of restriction. State capacity had a stronger influence on face-covering requirements and private-gathering restrictions than it had on school closures, workplace closures, and stay-at-home orders.

CONCLUSIONS

The way in which social distancing rules are applied is endogenous to state capacity. Effective governance is a precursor to more targeted and nuanced applications of social distancing rules.

The effectiveness of vaccination, testing, and lockdown strategies against COVID-19

The ability of various policy activities to reduce the reproduction rate of the COVID-19 disease is widely discussed. Using a stringency index that comprises a variety of lockdown levels, such as school and workplace closures, we analyze the effectiveness of government restrictions. At the same time, we investigate the capacity of a range of lockdown measures to lower the reproduction rate by considering vaccination rates and testing strategies. By including all three components in an SIR (Susceptible, Infected, Recovery) model, we show that a general and comprehensive test strategy is instrumental in reducing the spread of COVID-19. The empirical study demonstrates that testing and isolation represent a highly effective and preferable approach towards overcoming the pandemic, in particular until vaccination rates have risen to the point of herd immunity.

Estimating the instantaneous reproduction number (Rt) by using particle filter

Background

Monitoring the transmission of coronavirus disease 2019 (COVID-19) requires accurate estimation of the effective reproduction number (R t ). However, existing methods for calculating R t may yield biased estimates if important real-world factors, such as delays in confirmation, pre-symptomatic transmissions, or imperfect data observation, are not considered.

Method

To include real-world factors, we expanded the susceptible-exposed-infectious-recovered (SEIR) model by incorporating pre-symptomatic (P) and asymptomatic (A) states, creating the SEPIAR model. By utilizing both stochastic and deterministic versions of the model, and incorporating predetermined time series of R t , we generated simulated datasets that simulate real-world challenges in estimating R t . We then compared the performance of our proposed particle filtering method for estimating R t with the existing EpiEstim approach based on renewal equations.

Results

The particle filtering method accurately estimated R t even in the presence of data with delays, pre-symptomatic transmission, and imperfect observation. When evaluating via the root mean square error (RMSE) metric, the performance of the particle filtering method was better in general and was comparable to the EpiEstim approach if perfectly deconvolved infection time series were provided, and substantially better when R t exhibited short-term fluctuations and the data was right truncated.

Conclusions

The SEPIAR model, in conjunction with the particle filtering method, offers a reliable tool for predicting the transmission trend of COVID-19 and assessing the impact of intervention strategies. This approach enables enhanced monitoring of COVID-19 transmission and can inform public health policies aimed at controlling the spread of the disease.

Why Similar Policies Resulted In Different COVID-19 Outcomes: How Responsiveness And Culture Influenced Mortality Rates

In the first two years of the COVID-19 pandemic, per capita mortality varied by more than a hundredfold across countries, despite most implementing similar nonpharmaceutical interventions. Factors such as policy stringency, gross domestic product, and age distribution explain only a small fraction of mortality variation. To address this puzzle, we built on a previously validated pandemic model in which perceived risk altered societal responses affecting SARS-CoV-2 transmission. Using data from more than 100 countries, we found that a key factor explaining heterogeneous death rates was not the policy responses themselves but rather variation in responsiveness. Responsiveness measures how sensitive communities are to evolving mortality risks and how readily they adopt nonpharmaceutical interventions in response, to curb transmission. We further found that responsiveness correlated with two cultural constructs across countries: uncertainty avoidance and power distance. Our findings show that more responsive adoption of similar policies saves many lives, with important implications for the design and implementation of responses to future outbreaks.

Reproduction number projection for the COVID-19 pandemic

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 $\mathcal{R}_{0}$ R 0 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 $\mathcal{R}_{0}$ R 0 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 $\mathcal{R}_{0}$ R 0 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.

Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany

The effective reproductive number Rt has taken a central role in the scientific, political, and public discussion during the COVID-19 pandemic, with numerous real-time estimates of this quantity routinely published. Disagreement between estimates can be substantial and may lead to confusion among decision-makers and the general public. In this work, we compare different estimates of the national-level effective reproductive number of COVID-19 in Germany in 2020 and 2021. We consider the agreement between estimates from the same method but published at different time points (within-method agreement) as well as retrospective agreement across eight different approaches (between-method agreement). Concerning the former, estimates from some methods are very stable over time and hardly subject to revisions, while others display considerable fluctuations. To evaluate between-method agreement, we reproduce the estimates generated by different groups using a variety of statistical approaches, standardizing analytical choices to assess how they contribute to the observed disagreement. These analytical choices include the data source, data pre-processing, assumed generation time distribution, statistical tuning parameters, and various delay distributions. We find that in practice, these auxiliary choices in the estimation of Rt may affect results at least as strongly as the selection of the statistical approach. They should thus be communicated transparently along with the estimates.

Impact of COVID-19 on mortality in coastal Kenya: a longitudinal open cohort study

The mortality impact of COVID-19 in Africa remains controversial because most countries lack vital registration. We analysed excess mortality in Kilifi Health and Demographic Surveillance System, Kenya, using 9 years of baseline data. SARS-CoV-2 seroprevalence studies suggest most adults here were infected before May 2022. During 5 waves of COVID-19 (April 2020-May 2022) an overall excess mortality of 4.8% (95% PI 1.2%, 9.4%) concealed a significant excess (11.6%, 95% PI 5.9%, 18.9%) among older adults ( ≥ 65 years) and a deficit among children aged 1-14 years (-7.7%, 95% PI -20.9%, 6.9%). The excess mortality rate for January 2020-December 2021, age-standardised to the Kenyan population, was 27.4/100,000 person-years (95% CI 23.2-31.6). In Coastal Kenya, excess mortality during the pandemic was substantially lower than in most high-income countries but the significant excess mortality in older adults emphasizes the value of achieving high vaccine coverage in this risk group.

The evolution of age-specific smoking cessation rates in the United States from 2009 to 2017: a Kalman filter based approach

BACKGROUND

Tracking the US smoking cessation rate over time is of great interest to tobacco control researchers and policymakers since smoking cessation behaviors have a major effect on the public's health. Recent studies have employed dynamic models to estimate the US cessation rate through observed smoking prevalence. However, none of those studies has provided annual estimates of the cessation rate by age group. Hence, the primary objective of this study is to estimate annual smoking cessation rates specific to different age groups in the US from 2009 to 2017.

METHODS

We employed a Kalman filter approach to investigate the annual evolution of age-group-specific cessation rates, unknown parameters of a mathematical model of smoking prevalence, during the 2009-2017 period using data from the 2009-2018 National Health Interview Surveys. We focused on cessation rates in the 25-44, 45-64 and 65 + age groups.

RESULTS

The findings show that cessation rates followed a consistent u-shaped curve over time with respect to age (i.e., higher among the 25-44 and 65 + age groups, and lower among 45-64-year-olds). Over the course of the study, the cessation rates in the 25-44 and 65 + age groups remained nearly unchanged around 4.5% and 5.6%, respectively. However, the rate in the 45-64 age group exhibited a substantial increase of 70%, from 2.5% to 2009 to 4.2% in 2017. The estimated cessation rates in all three age groups tended to converge to the weighted average cessation rate over time.

CONCLUSIONS

The Kalman filter approach offers a real-time estimation of cessation rates that can be helpful for monitoring smoking cessation behavior.

A nationwide lockdown and deaths due to COVID-19 in the Indian subcontinent

During the COVID-19 pandemic, the effects of nationwide lockdowns on health outcomes have been widely studied in Western, developed countries. However, the effects of lockdowns in emerging and developing countries are largely unknown. We used data from India and Bangladesh to study the effect of nationwide restrictions on public movement in Bangladesh in April 2021 on health outcomes, specifically COVID-19 incidence and mortality. India and Bangladesh had nearly identical development of the COVID-19 Delta wave the weeks before the lockdown. We leveraged longitudinal data from the pre- and post-intervention period in both countries in a structural causal model, suggesting that the reported deaths in Bangladesh due to COVID-19 would have been ∼117% higher (95% PI: 72%-170%) in April 2021 had there been fewer restrictions. Further, we used population mobility data from Google to study behavioural changes in the two countries, supporting the hypothesis that the intervention had substantial effects on the mobility trends of the Bangladeshi population, which in turn reduced the number of COVID-19 deaths.

Governmental anti-pandemic policies, vaccination, population mobility, Twitter narratives, and the spread of COVID-19: Evidence from the European Union countries

We provide large-scale empirical evidence on the effects of multiple governmental regulatory and health policies, vaccination, population mobility, and COVID-19-related Twitter narratives on the spread of a new coronavirus infection. Using multiple-level fixed effects panel data model with weekly data for 27 European Union countries in the period of March 2020-June 2021, we show that governmental response policies were effective both in reducing the number of COVID-19 infection cases and deaths from it, particularly, in the countries with higher level of rule of law. Vaccination expectedly helped to decrease the number of virus cases. Reductions in population mobility in public places and workplaces were also powerful in fighting the pandemic. Next, we identify four core pandemic-related Twitter narratives: governmental response policies, people's sad feelings during the pandemic, vaccination, and pandemic-related international politics. We find that sad feelings' narrative helped to combat the virus spread in EU countries. Our findings also reveal that while in countries with high rule of law international politics' narrative helped to reduce the virus spread, in countries with low rule of law the effect was strictly the opposite. The latter finding suggests that trust in politicians played an important role in confronting the pandemic.

Instantaneous reproduction number and epidemic growth rate for predicting COVID-19 waves: the first 2 years of the pandemic in Spain

Introduction

Several indicators were employed to manage the COVID-19 pandemic. In this study, our objective was to compare the instantaneous reproductive number and the epidemic growth rate in the Spanish population.

Methods

Data on daily numbers of cases, admissions into hospitals, admissions into ICUs, and deaths due to COVID-19 in Spain from March 2020 to March 2022 were obtained. The four "pandemic state indicators", which are daily numbers of cases, admissions into hospitals, admissions into ICUs, and deaths due to COVID-19 in Spain from March 2020 to March 2022 were obtained from the Instituto de Salud Carlos III. The epidemic growth rate was estimated as the derivative of the natural logarithm of daily cases with respect to time. Both the reproductive number and the growth rate, as "pandemic trend indicators," were evaluated according to their capacity to anticipate waves as "pandemic state indicators."

Results

Using both the reproductive number and the epidemic growth rate, we were able to anticipate most COVID-19 waves. In most waves, the more severe the presentation of COVID-19, the more effective the pandemic trend indicators would be.

Conclusion

Besides daily number of cases or other measures of disease frequency, the epidemic growth rate and the reproductive number have different roles in measuring the trend of an epidemic. Naïve interpretations and the use of any indicator as a unique value to make decisions should be discouraged.

Endemic characteristics of SARS-CoV-2 infection

The fourth year of the COVID-19 pandemic without decreasing trends in the global numbers of new daily cases, high numbers of circulating SARS-CoV-2 variants and re-infections together with pessimistic predictions for the Omicron wave duration force studies about the endemic stage of the disease. The global trends were illustrated with the use the accumulated numbers of laboratory-confirmed COVID-19 cases and deaths, the percentages of fully vaccinated people and boosters (additional vaccinations), and the results of calculation of the effective reproduction number provided by Johns Hopkins University. A new modified SIR model with re-infections was proposed and analyzed. The estimated parameters of equilibrium show that the global numbers of new daily cases will range between 300 thousand and one million, daily deaths-between one and 3.3 thousand.

Improvement of the software for modeling the dynamics of epidemics and developing a user-friendly interface

The challenges humanity is facing due to the Covid-19 pandemic require timely and accurate forecasting of the dynamics of various epidemics to minimize the negative consequences for public health and the economy. One can use a variety of well-known and new mathematical models, taking into account a huge number of factors. However, complex models contain a large number of unknown parameters, the values of which must be determined using a limited number of observations, e.g., the daily datasets for the accumulated number of cases. Successful experience in modeling the COVID-19 pandemic has shown that it is possible to apply the simplest SIR model, which contains 4 unknown parameters. Application of the original algorithm of the model parameter identification for the first waves of the COVID-19 pandemic in China, South Korea, Austria, Italy, Germany, France, Spain has shown its high accuracy in predicting their duration and number of diseases. To simulate different epidemic waves and take into account the incompleteness of statistical data, the generalized SIR model and algorithms for determining the values of its parameters were proposed. The interference of the previous waves, changes in testing levels, quarantine or social behavior require constant monitoring of the epidemic dynamics and performing SIR simulations as often as possible with the use of a user-friendly interface. Such tool will allow predicting the dynamics of any epidemic using the data on the number of diseases over a limited period (e.g., 14 days). It will be possible to predict the daily number of new cases for the country as a whole or for its separate region, to estimate the number of carriers of the infection and the probability of facing such a carrier, as well as to estimate the number of deaths. Results of three SIR simulations of the COVID-19 epidemic wave in Japan in the summer of 2022 are presented and discussed. The predicted accumulated and daily numbers of cases agree with the results of observations, especially for the simulation based on the datasets corresponding to the period from July 3 to July 16, 2022. A user-friendly interface also has to ensure an opportunity to compare the epidemic dynamics in different countries/regions and in different years in order to estimate the impact of vaccination levels, quarantine restrictions, social behavior, etc. on the numbers of new infections, death, and mortality rates. As example, the comparison of the COVID-19 pandemic dynamics in Japan in the summer of 2020, 2021 and 2022 is presented. The high level of vaccinations achieved in the summer of 2022 did not save Japan from a powerful pandemic wave. The daily numbers of cases were about ten times higher than in the corresponding period of 2021. Nevertheless, the death per case ratio in 2022 was much lower than in 2020.

COWAVE: A labelled COVID-19 wave dataset for building predictive models

The ongoing COVID-19 pandemic has posed a significant global challenge to healthcare systems. Every country has seen multiple waves of this disease, placing a considerable strain on healthcare resources. Across the world, the pandemic has motivated diligent data collection, with an enormous amount of data being available in the public domain. In this manuscript, we collate COVID-19 case data from around the world (available on the World Health Organization (WHO) website), and provide various definitions for waves. Using these definitions to define labels, we create a labelled dataset, which can be used while building supervised learning classifiers. We also use a simple eXtreme Gradient Boosting (XGBoost) model to provide a minimum standard for future classifiers trained on this dataset and demonstrate the utility of our dataset for the prediction of (future) waves. This dataset will be a valuable resource for epidemiologists and others interested in the early prediction of future waves. The datasets are available from https://github.com/RamanLab/COWAVE/.

Correlations of mobility and Covid-19 transmission in global data

Assessing the contribution of mobility declines to the control of Covid-19 diffusion is an urgent challenge of global import. We analyze the temporal correlation between transmission rates and societal mobility levels using daily mobility data from Google and Apple in an international panel of 99 countries during the period of March-December 2020. Reduced form regression estimates that flexibly control for time trends suggest that globally, a 10 percentage point reduction in mobility is associated with a 0.05-0.07 reduction in the value of the effective reproduction number, R(t). However, the strength of the association varies substantially across world regions and over time, being initially positive and strong in most world regions during the 2020 spring period, but becoming weaker over the summer, especially in Europe and Asia. We further find evidence that the strength of the association between mobility and transmission rates is reduced where facial coverings rules were implemented.

Informed Random Forest to Model Associations of Epidemiological Priors, Government Policies, and Public Mobility

Background. Infectious diseases constitute a significant concern worldwide due to their increasing prevalence, associated health risks, and the socioeconomic costs. Machine learning (ML) models and epidemic models formulated using deterministic differential equations are the most dominant tools for analyzing and modeling the transmission of infectious diseases. However, ML models can be inconsistent in extracting the dynamics of a disease in the presence of data drifts. Likewise, the capability of epidemic models is constrained to parameter dimensions and estimation. We aimed at creating a framework of informed ML that integrates a random forest (RF) with an adapted susceptible infectious recovered (SIR) model to account for accuracy and consistency in stochasticity within the dynamics of coronavirus disease 2019 (COVID-19). Methods. An adapted SIR model was used to inform a default RF on predicting new COVID-19 cases (NCCs) at given intervals. We validated the performance of the informed RF (IRF) using real data. We used Botswana's pharmaceutical interventions (PIs) and non-PIs (NPIs) adopted between February 2020 and August 2022. The discrepancy between predictions and observations is modeled using loss functions, which are minimized, interpreted, and used to assess the IRF. Results. The findings on the real data have revealed the effectiveness of the default RF in modeling and predicting NCCs. The use of the effective reproductive rate to inform the RF yielded an excellent predictive power (84%) compared with 75% by the default RF. Conclusion. This research has potential to inform policy and decision makers in developing systems to evaluate interventions for infectious diseases.

Highlights

This framework is initiated by incorporating model outputs from an epidemic model to a machine learning model.An informed random forest (RF) is instantiated to model government and public responses to the COVID-19 pandemic.This framework does not require data transformations, and the epidemic model is shown to boost the RF's performance.This is a baseline knowledge-informed learning framework for assessing public health interventions in Botswana.

Model based on COVID-19 evidence to predict and improve pandemic control

Based on the extensive data accumulated during the COVID-19 pandemic, we put forward simple to implement indicators, that should alert authorities and provide early warnings of an impending sanitary crisis. In fact, Testing, Tracing, and Isolation (TTI) in conjunction with disciplined social distancing and vaccination were expected to achieve negligible COVID-19 contagion levels; however, they proved to be insufficient, and their implementation has led to controversial social, economic and ethical challenges. This paper focuses on the development of simple indicators, based on the experience gained by COVID-19 data, which provide a sort of yellow light as to when an epidemic might expand, despite some short term decrements. We show that if case growth is not stopped during the 7 to 14 days after onset, the growth risk increases considerably, and warrants immediate attention. Our model examines not only the COVID contagion propagation speed, but also how it accelerates as a function of time. We identify trends that emerge under the various policies that were applied, as well as their differences among countries. The data for all countries was obtained from ourworldindata.org. Our main conclusion is that if the reduction spread is lost during one, or at most two weeks, urgent measures should be implemented to avoid scenarios in which the epidemic gains strong impetus.

Forecasting and what-if analysis of new positive COVID-19 cases during the first three waves in Italy

The joint exploitation of data related to epidemiological, mobility, and restriction aspects of COVID-19 with machine learning algorithms can support the development of predictive models that can be used to forecast new positive cases and study the impact of more or less severe restrictions. In this work, we integrate heterogeneous data from several sources and solve a multivariate time series forecasting task, specifically targeting the Italian case at both national and regional levels, during the first three waves of the pandemic. The goal is to build a robust predictive model to predict the number of new cases over a given time horizon so that any restrictive actions can be better planned. In addition, we perform a what-if analysis based on the best-identified predictive models to evaluate the impact of specific restrictions on the trend of positive cases. Our focus on the first three waves is motivated by the fact that it represents a typical emergency scenario (when no stable cure or vaccine is available) that may occur when a new pandemic spreads. Our experimental results prove that exploiting the considered heterogeneous data leads to accurate predictive models, reaching a WAPE of 5.75% at the national level. Furthermore, in the subsequent what-if analysis, we observed that strong all-in-one initiatives, such as total lockdowns, may not be adequate, while more specific and targeted solutions should be adopted. The developed models can help policy and decision-makers better plan intervention strategies and retrospectively analyze the effects of the decisions made at different scales. Joint exploitation of data on epidemiological, mobility, and restriction aspects of COVID-19 with machine learning algorithms to learn predictive models to forecast new positive cases.

Public interest in different types of masks and its relationship with pandemic and policy measures during the COVID-19 pandemic: a study using Google Trends data

Google Trends data have been used to investigate various themes on online information seeking. It was unclear if the population from different parts of the world shared the same amount of attention to different mask types during the COVID-19 pandemic. This study aimed to reveal which types of masks were frequently searched by the public in different countries, and evaluated if public attention to masks could be related to mandatory policy, stringency of the policy, and transmission rate of COVID-19. By referring to an open dataset hosted at the online database Our World in Data, the 10 countries with the highest total number of COVID-19 cases as of 9th of February 2022 were identified. For each of these countries, the weekly new cases per million population, reproduction rate (of COVID-19), stringency index, and face covering policy score were computed from the raw daily data. Google Trends were queried to extract the relative search volume (RSV) for different types of masks from each of these countries. Results found that Google searches for N95 masks were predominant in India, whereas surgical masks were predominant in Russia, FFP2 masks were predominant in Spain, and cloth masks were predominant in both France and United Kingdom. The United States, Brazil, Germany, and Turkey had two predominant types of mask. The online searching behavior for masks markedly varied across countries. For most of the surveyed countries, the online searching for masks peaked during the first wave of COVID-19 pandemic before the government implemented mandatory mask wearing. The search for masks positively correlated with the government response stringency index but not with the COVID-19 reproduction rate or the new cases per million.

Bayesian Noise Modelling for State Estimation of the Spread of COVID-19 in Saudi Arabia with Extended Kalman Filters

The epistemic uncertainty in coronavirus disease (COVID-19) model-based predictions using complex noisy data greatly affects the accuracy of pandemic trend and state estimations. Quantifying the uncertainty of COVID-19 trends caused by different unobserved hidden variables is needed to evaluate the accuracy of the predictions for complex compartmental epidemiological models. A new approach for estimating the measurement noise covariance from real COVID-19 pandemic data has been presented based on the marginal likelihood (Bayesian evidence) for Bayesian model selection of the stochastic part of the Extended Kalman filter (EKF), with a sixth-order nonlinear epidemic model, known as the SEIQRD (Susceptible-Exposed-Infected-Quarantined-Recovered-Dead) compartmental model. This study presents a method for testing the noise covariance in cases of dependence or independence between the infected and death errors, to better understand their impact on the predictive accuracy and reliability of EKF statistical models. The proposed approach is able to reduce the error in the quantity of interest compared to the arbitrarily chosen values in the EKF estimation.

Epidemiological waves - Types, drivers and modulators in the COVID-19 pandemic

Introduction

A discussion of 'waves' of the COVID-19 epidemic in different countries is a part of the national conversation for many, but there is no hard and fast means of delineating these waves in the available data and their connection to waves in the sense of mathematical epidemiology is only tenuous.

Methods

We present an algorithm which processes a general time series to identify substantial, significant and sustained periods of increase in the value of the time series, which could reasonably be described as 'observed waves'. This provides an objective means of describing observed waves in time series. We use this method to synthesize evidence across different countries to study types, drivers and modulators of waves.

Results

The output of the algorithm as applied to epidemiological time series related to COVID-19 corresponds to visual intuition and expert opinion. Inspecting the results of individual countries shows how consecutive observed waves can differ greatly with respect to the case fatality ratio. Furthermore, in large countries, a more detailed analysis shows that consecutive observed waves have different geographical ranges. We also show how waves can be modulated by government interventions and find that early implementation of NPIs correlates with a reduced number of observed waves and reduced mortality burden in those waves.

Conclusion

It is possible to identify observed waves of disease by algorithmic methods and the results can be fruitfully used to analyse the progression of the epidemic.

Coupled disease-vaccination behavior dynamic analysis and its application in COVID-19 pandemic

Predicting the evolutionary dynamics of the COVID-19 pandemic is a complex challenge. The complexity increases when the vaccination process dynamic is also considered. In addition, when applying a voluntary vaccination policy, the simultaneous behavioral evolution of individuals who decide whether and when to vaccinate must be included. In this paper, a coupled disease-vaccination behavior dynamic model is introduced to study the coevolution of individual vaccination strategies and infection spreading. We study disease transmission by a mean-field compartment model and introduce a non-linear infection rate that takes into account the simultaneity of interactions. Besides, the evolutionary game theory is used to investigate the contemporary evolution of vaccination strategies. Our findings suggest that sharing information with the entire population about the negative and positive consequences of infection and vaccination is beneficial as it boosts behaviors that can reduce the final epidemic size. Finally, we validate our transmission mechanism on real data from the COVID-19 pandemic in France.

Lab-on-a-chip for the easy and visual detection of SARS-CoV-2 in saliva based on sensory polymers

The initial stages of the pandemic caused by SARS-CoV-2 showed that early detection of the virus in a simple way is the best tool until the development of vaccines. Many different tests are invasive or need the patient to cough up or even drag a sample of mucus from the throat area. Besides, the manufacturing time has proven insufficient in pandemic conditions since they were out of stock in many countries. Here we show a new method of manufacturing virus sensors and a proof of concept with SARS-CoV-2. We found that a fluorogenic peptide substrate of the main protease of the virus (Mpro) can be covalently immobilized in a polymer, with which a cellulose-based material can be coated. These sensory labels fluoresce with a single saliva sample of a positive COVID-19 patient. The results matched with that of the antigen tests in 22 of 26 studied cases (85% success rate).

Depression risk among community-dwelling older people is associated with perceived COVID-19 infection risk: effects of news report latency and focusing on number of infected cases

Awareness of COVID-19 infection risk and oscillation patterns ('waves') may affect older people's mental health. Empirical data from populations experiencing multiple waves of community outbreaks can inform guidance for maintaining mental health. This study aims to investigate the effects of COVID-19 infection risk and oscillations on depression among community-dwelling older people in Hong Kong.

A rolling cross-sectional telephone survey method was used. Screening for depression risk was conducted among 8,163 older people (age ≥ 60) using the Patient Health Questionnaire-2 (PHQ-2) from February to August 2020. The relationships between PHQ-2, COVID-19 infection risk proxies - change in newly infected cases and effective reproductive number (R_t), and oscillations - stage of a 'wave' reported in the media, were analysed using correlation and regression.

8.4% of survey respondents screened positive for depression risk. Being female (β = .08), having a pre-existing mental health issue (β = .21), change in newly infected cases (β = .05), and screening during the latency period before the media called out new waves (β = .03), contributed to higher depression risk (R² = .06, all p <.01).

While depression risk does not appear alarming in this sample, our results highlight that older people are sensitive to reporting of infection, particularly among those with existing mental health needs. Future public health communication should balance awareness of infection risks with mental health protection.

A model of behavioural response to risk accurately predicts the statistical distribution of COVID-19 infection and reproduction numbers

One clear aspect of behaviour in the COVID-19 pandemic has been people's focus on, and response to, reported or observed infection numbers in their community. We describe a simple model of infectious disease spread in a pandemic situation where people's behaviour is influenced by the current risk of infection and where this behavioural response acts homeostatically to return infection risk to a certain preferred level. This homeostatic response is active until approximate herd immunity is reached: in this domain the model predicts that the reproduction rate R will be centred around a median of 1, that proportional change in infection numbers will follow the standard Cauchy distribution with location and scale parameters 0 and 1, and that high infection numbers will follow a power-law frequency distribution with exponent 2. To test these predictions we used worldwide COVID-19 data from 1st February 2020 to 30th June 2022 to calculate [Formula: see text] confidence interval estimates across countries for these R, location, scale and exponent parameters. The resulting median R estimate was [Formula: see text] (predicted value 1) the proportional change location estimate was [Formula: see text] (predicted value 0), the proportional change scale estimate was [Formula: see text] (predicted value 1), and the frequency distribution exponent estimate was [Formula: see text] (predicted value 2); in each case the observed estimate agreed with model predictions.

Modelling Aotearoa New Zealand's COVID-19 protection framework and the transition away from the elimination strategy

For the first 18 months of the COVID-19 pandemic, New Zealand used an elimination strategy to suppress community transmission of SARS-CoV-2 to zero or very low levels. In late 2021, high vaccine coverage enabled the country to transition away from the elimination strategy to a mitigation strategy. However, given negligible levels of immunity from prior infection, this required careful planning and an effective public health response to avoid uncontrolled outbreaks and unmanageable health impacts. Here, we develop an age-structured model for the Delta variant of SARS-CoV-2 including the effects of vaccination, case isolation, contact tracing, border controls and population-wide control measures. We use this model to investigate how epidemic trajectories may respond to different control strategies, and to explore trade-offs between restrictions in the community and restrictions at the border. We find that a low case tolerance strategy, with a quick change to stricter public health measures in response to increasing cases, reduced the health burden by a factor of three relative to a high tolerance strategy, but almost tripled the time spent in national lockdowns. Increasing the number of border arrivals was found to have a negligible effect on health burden once high vaccination rates were achieved and community transmission was widespread.

From Heroes to Scoundrels: Exploring the effects of online campaigns celebrating frontline workers on COVID-19 outcomes

This paper examines the effects of online campaigns celebrating frontline workers on COVID-19 outcomes regarding new cases, deaths, and vaccinations, using the United Kingdom as a case study. We implement text and sentiment analysis on Twitter data and feed the result into random regression forests and cointegration analysis. Our combined machine learning and econometric approach shows very weak effects of both the volume and the sentiment of Twitter discussions on new cases, deaths, and vaccinations. On the other hand, established relationships (such as between stringency measures and cases/deaths and between vaccinations and deaths) are confirmed. On the contrary, we find adverse lagged effects from negative sentiment to vaccinations and from new cases to negative sentiment posts. As we assess the knowledge acquired from the COVID-19 crisis, our findings can be used by policy makers, particularly in public health, and prepare for the next pandemic.

Analysis of COVID-19 in Japan with extended SEIR model and ensemble Kalman filter

We introduce an extended SEIR infectious disease model with data assimilation for the study of the spread of COVID-19. In this framework, undetected asymptomatic and pre-symptomatic cases are taken into account, and the impact of their uncertain proportion is fully investigated. The standard SEIR model does not consider these populations, while their role in the propagation of the disease is acknowledged. An ensemble Kalman filter is implemented to assimilate reliable observations of three compartments in the model. The system tracks the evolution of the effective reproduction number and estimates the unobservable subpopulations. The analysis is carried out for three main prefectures of Japan and for the entire country of Japan. For these four communities, our estimated effective reproduction numbers are more stable than the corresponding ones estimated by a different method (Toyokeizai). We also perform sensitivity tests for different values of some uncertain medical parameters, like the relative infectivity of symptomatic/asymptomatic cases. The regional analysis results suggest the decreasing efficiency of the states of emergency.

Epidemic responses under uncertainty

We examine how policymakers react to a pandemic with uncertainty around key epidemiological and economic policy parameters by embedding a macroeconomic SIR model in a robust control framework. Uncertainty about disease virulence and severity leads to stricter and more persistent quarantines, while uncertainty about the economic costs of mitigation leads to less stringent quarantines. On net, an uncertainty-averse planner adopts stronger mitigation measures. Intuitively, the cost of underestimating the pandemic is out-of-control growth and permanent loss of life, while the cost of underestimating the economic consequences of quarantine is more transitory.

An improved epidemiological-unscented Kalman filter (hybrid SEIHCRDV-UKF) model for the prediction of COVID-19. Application on real-time data

The prevalence of COVID-19 has been the most serious health challenge of the 21th century to date, concerning national health systems on a daily basis, since December 2019 when it appeared in Wuhan City. Nevertheless, most of the proposed mathematical methodologies aiming to describe the dynamics of an epidemic, rely on deterministic models that are not able to reflect the true nature of its spread. In this paper, we propose a SEIHCRDV model - an extension/improvement of the classic SIR compartmental model - which also takes into consideration the populations of exposed, hospitalized, admitted in intensive care units (ICU), deceased and vaccinated cases, in combination with an unscented Kalman filter (UKF), providing a dynamic estimation of the time dependent system's parameters. The stochastic approach is considered necessary, as both observations and system equations are characterized by uncertainties. Apparently, this new consideration is useful for examining various pandemics more effectively. The reliability of the model is examined on the daily recordings of COVID-19 in France, over a long period of 265 days. Two major waves of infection are observed, starting in January 2021, which signified the start of vaccinations in Europe providing quite encouraging predictive performance, based on the produced NRMSE values. Special emphasis is placed on proving the non-negativity of SEIHCRDV model, achieving a representative basic reproductive number R 0 and demonstrating the existence and stability of disease equilibria according to the formula produced to estimate R 0 . The model outperforms in predictive ability not only deterministic approaches but also state-of-the-art stochastic models that employ Kalman filters. Furthermore, the relevant analysis supports the importance of vaccination, as even a small increase in the dialy vaccination rate could lead to a notable reduction in mortality and hospitalizations.

How to go viral: A COVID-19 model with endogenously time-varying parameters

We estimate a panel model with endogenously time-varying parameters for COVID-19 cases and deaths in U.S. states. The functional form for infections incorporates important features of epidemiological models but is flexibly parameterized to capture different trajectories of the pandemic. Daily deaths are modeled as a spike-and-slab regression on lagged cases. Our Bayesian estimation reveals that social distancing and testing have significant effects on the parameters. For example, a 10 percentage point increase in the positive test rate is associated with a 2 percentage point increase in the death rate among reported cases. The model forecasts perform well, even relative to models from epidemiology and statistics.