Course Of Epidemic Research Articles

Natural and socio-cultural factors of epidemics (plague, smallpox): The example of the Adyghe population of the Northwest Caucasus

Background. The article discusses the problem of the impact of epidemic diseases on ethnogenesis. As an ancient agricultural ethnic group in contact with nomadic and Mediterranean populations, the Adyghe were formed during periods of frequent epidemics of dangerous diseases caused by the process of animal domestication and subsequent impact of anthropogenic factors. The devastating effect of smallpox and measles pandemics was felt following the establishment of intensive trade exchange during the 2nd century CE by the Roman and the Han empires. The first plague pandemic (beginning with the “Justinianic Plague”, 542–543) mostly affected the Balkans and the Eastern Mediterranean. The second plague pandemic, the so-called “Black Death” (1346–1353), which appeared in the center of the Mongol Empire, quickly spread via caravan routes across most of Eurasia. The scale of the epidemic was influenced by the pathogenic complex that had developed by the 14th century, which included humans, the bacterial pathogenic agent itself, and its carriers (large rodents especially black rats, fleas). Factors such as famine caused by overpopulation, low standards of personal and public hygiene, the prevalence of wooden buildings in cities and towns, the fur trade, etc., also had contributory effects. Objectives. The study set out to identify the natural and the socio-cultural factors that influenced the spread of the plague and the smallpox epidemics in the North-West Caucasus. Methods. The data on which this study is based are formed from collections of narrative sources (Russian, Arabic-Persian, European), archaeological and historical-anthropological works, medical and genetic studies (electronic platforms: eLIBRARY.RU, Academia.edu, CyberLeninka, J-STAGE [Japan Science Technology Information Aggregator, Electronic]). Research methods: historical and comparative, on which basis a comparative analysis of the impact of the plague pandemic of the mid-14th century centering on contact between on nomadic and sedentary ethnic groups (Mongols and Russians, Mongols and Circassians) was carried out; the individualizing comparison method, which was used to identify the specific means by which the Circassian ethnic group overcame the Black Death pandemic; the systems analysis method, on which basis the roles of pathogenic complex and anthropogenic factors in the course of epidemics were compared. Results. The results of the analysis show that the population of the North-West Caucasus were affected by the Black Death plague of the mid-14th century to a significantly lesser extent than the urban centers of the Golden Horde, Rus’ and Europe. The serendipitous failure of the pathogenic complex in the former region was due to the following natural and anthropogenic factors: (1) the North-West Caucasus (more precisely, the entire area to the west of the Teberda River) was naturally bereft of effective carriers of the plague bacillus (marmots, gophers, etc.); (2) Matrega and Copa, the two contemporaneous Circassian towns serving as ports for the shipment of grain and other agricultural products, were located on the periphery of the Adyghe settlement area; (3) the dispersed, farmstead way of life among the estuaries and the complex network of peninsulas of the Kuban Delta, mountains and forests of the NWC contributed to the rapid adoption of quarantine measures; (4) a reliable vitamin-rich nutrition system (sturgeon and their caviar, goat meat and cheese, fruit, honey, etc.); (5) a well-developed system of hygiene and disease prevention (smallpox, malaria), whose existence was retrospectively established based on sources from the 17th–19th centuries. Conclusion. The plague pandemic that took place within the unified space of Mongol imperial communications had a huge impact on the historical fate of the Adyghe ethnic group, which was manifested in their paradoxical numerical growth against the backdrop of general depopulation. Against the backdrop of frequent epidemics, Adyghe developed strict and quite effective rules for preserving life and health, covering disease prevention, personal and public hygiene. Some particularly impressive practices, if not created by Adyghe folk medicine, then preserved there and later transmitted to Ottoman Turkey and Western Europe, was manifested in the original practice of smallpox inoculation.

Emergence of the reproduction matrix in epidemic forecasting.

During the recent COVID-19 pandemic, the instantaneous reproduction number, R(t), has surged as a widely used measure to target public health interventions aiming at curbing the infection rate. In analogy with the basic reproduction number that arises from the linear stability analysis, R(t) is typically interpreted as a threshold parameter that separates exponential growth (R(t) > 1) from exponential decay (R(t) < 1). In real epidemics, however, the finite number of susceptibles, the stratification of the population (e.g. by age or vaccination state), and heterogeneous mixing lead to more complex epidemic courses. In the context of the multidimensional renewal equation, we generalize the scalar R(t) to a reproduction matrix, [Formula: see text], which details the epidemic state of the stratified population, and offers a concise epidemic forecasting scheme. First, the reproduction matrix is computed from the available incidence data (subject to some a priori assumptions), then it is projected into the future by a transfer functional to predict the epidemic course. We demonstrate that this simple scheme allows realistic and accurate epidemic trajectories both in synthetic test cases and with reported incidence data from the COVID-19 pandemic. Accounting for the full heterogeneity and nonlinearity of the infection process, the reproduction matrix improves the prediction of the infection peak. In contrast, the scalar reproduction number overestimates the possibility of sustaining the initial infection rate and leads to an overshoot in the incidence peak. Besides its simplicity, the devised forecasting scheme offers rich flexibility to be generalized to time-dependent mitigation measures, contact rate, infectivity and vaccine protection.

The End of the Beginning? Temporality and Bioagency in Pandemic Research.

This paper deals with the ways in which the intellectual and political history of AIDS can assist in the chronological conceptualization of a pandemic such as COVID-19 as it is unfolding. It problematizes the idea of pandemic "beginnings" and "ends" to show that such definitions are shaped by the disciplinary location and thematic foci of relevant scholars. Central to this analysis is the notion that ethical and political contexts affect research on a pandemic in different ways at national and global levels at various points in its trajectory. The article develops this argument in relation to two main themes: firstly, with reference to the history of AIDS research in South Africa; secondly, with the philosophical concept of bioagency to understand the ways in which viruses and humans co-shape the course of epidemics over time. I first make the case for the development of historically informed, long-term ethnographic studies of COVID-19. Using bioagency as a point of departure to consider viruses as social actors, the essay then critiques the notion of bioinformationalism as catalyzing the widening accessibility of biomedical research. Instead, I discuss the biotechnology and pharmaceutical industries as protagonists in the operation of biocapital. I argue that the history of AIDS in South Africa can provide methodological and theoretical insights into how to interpret an unfolding epidemic, outlining an ambitious transdisciplinary research agenda for thinking about the temporality of a pandemic spanning the different, interconnected, scales of life.

Inferring COVID-19 testing and vaccination behavior from New Jersey testing data

Characterizing the relationship between disease testing behaviors and infectious disease dynamics is of great importance for public health. Tests for both current and past infection can influence disease-related behaviors at the individual level, while population-level knowledge of an epidemic's course may feed back to affect one's likelihood of taking a test. The COVID-19 pandemic has generated testing data on an unprecedented scale for tests detecting both current infection (PCR, antigen) and past infection (serology); this opens the way to characterizing the complex relationship between testing behavior and infection dynamics. Leveraging a rich database of individualized COVID-19 testing histories in New Jersey, we analyze the behavioral relationships between PCR and serology tests, infection, and vaccination. We quantify interactions between individuals' test-taking tendencies and their past testing and infection histories, finding that PCR tests were disproportionately taken by people currently infected, and serology tests were disproportionately taken by people with past infection or vaccination. The effects of previous positive test results on testing behavior are less consistent, as individuals with past PCR positives were more likely to take subsequent PCR and serology tests at some periods of the epidemic time course and less likely at others. Lastly, we fit a model to the titer values collected from serology tests to infer vaccination trends, finding a marked decrease in vaccination rates among individuals who had previously received a positive PCR test. These results exemplify the utility of individualized testing histories in uncovering hidden behavioral variables affecting testing and vaccination.

Change in Age profile of Respiratory Syncytial Virus disease over the course of annual epidemics: a multi-national study

We aimed to study whether the percentwise age distribution of RSV cases changes over time during annual epidemics. We used surveillance data (2008-2019) from the Netherlands, Lyon (France), Portugal, Singapore, Ecuador, South Africa, and New Zealand. In each country, every season was divided into "epidemic quarters", i.e. periods corresponding to each quartile of RSV cases. Multinomial logistic regression models were fitted to evaluate whether the likelihood of RSV cases being aged <1 or ≥5years (vs. 1 to <5) changed over time within a season. In all countries, RSV cases were significantly more likely to be aged <1year in the 4th vs. 1st epidemic quarter; the relative risk ratio [RRR] ranged between 1.35 and 2.56. Likewise, RSV cases were significantly more likely to be aged ≥5years in the 4th vs. 1st epidemic quarter (except in Singapore); the RRR ranged from 1.75 to 6.70. The results did not change when stratifying by level of care or moving the lower cut-off to 6months. The age profile of RSV cases shifts within a season, with infants and adolescents, adults, and the elderly constituting a higher proportion of cases in the later phases of annual epidemics. These findings may have implications for RSV prevention policies with newly approved vaccines.

Disease avoidance threatens social cohesion in a large-scale social networking experiment

People tend to limit social contacts during times of increased health risks, leading to disruption of social networks thus changing the course of epidemics. To what extent, however, do people show such avoidance reactions? To test the predictions and assumptions of an agent-based model on the feedback loop between avoidance behavior, social networks, and disease spread, we conducted a large-scale (2,879 participants) incentivized experiment. The experiment rewards maintaining social relations and structures, and penalizes acquiring infections. We find that disease avoidance dominates networking decisions, despite relatively low penalties for infections; and that participants use more sophisticated strategies than expected (e.g., avoiding susceptible others with infectious neighbors), while they forget to maintain a beneficial network structure. Consequently, we observe low infection numbers, but also deterioration of network positions. These results imply that the focus on a more obvious signal (i.e., infection) may lead to unwanted side effects (i.e., loss of social cohesion).

889. The age of RSV cases changes consistently over the course of annual epidemics: potential insights into RSV transmission dynamics based on surveillance data from seven countries.

Abstract Background RSV infections affects people of all ages but the seasonality of RSV among different age groups have not been thoroughly examined. In this study, we investigated changes in the age distribution of RSV cases during the course of annual epidemics. Methods We analyzed surveillance data (2008-2019) from the Netherlands, the city of Lyon (France), Portugal, Singapore, Ecuador, South Africa, and New Zealand using the Global Epidemiology of RSV (GERi) study database. We defined annual RSV seasons as the period from July to June in the Northern hemisphere, or the calendar year in the tropics and Southern hemisphere. Within each season, we divided the data into “epidemic quartiles”, corresponding to each quartile of RSV cases in the season. We used multilevel multinomial logistic regression models with season as the higher-level variable to assess whether the likelihood of RSV cases being aged &amp;lt; 1 year or ≥5 years (vs. those aged 1 to &amp;lt; 5 years) changed over time during the season. Results A total of 27,391 RSV cases were included. Across all countries, the odds of RSV cases being aged &amp;lt; 1 year (vs. 1 to &amp;lt; 5 years) were significantly higher in the fourth epidemic quartile compared to the first quartile; the relative risk ratio (RRR) ranged between 1.35 in Lyon, France, and 2.56 in the Netherlands. Similarly, the odds of RSV cases being aged ≥5 years (vs. 1 to &amp;lt; 5 years) were significantly higher in the fourth epidemic quartile compared to the first quartile (except in Singapore); the RRR ranged from 1.75 in Ecuador to 6.70 in Lyon, France. The results were consistent when stratifying the data by level of care (primary vs. secondary) or using a lower cut-off of six months of age. Conclusion The age distribution of RSV cases changes over the course of a season, with infants (&amp;lt; 1 year) and older children (≥5 years), adults, and elderly people constituting a higher proportion of RSV cases in the later phases of annual epidemics. These findings provide potential insights into the transmission dynamics of RSV and may inform prevention and control measures, e.g. the implementation of targeted age-specific interventions. Funding: This research was funded by a collaborative agreement with Sanofi / AstraZeneca and is part of the RODEO research program for RSV disease. Disclosures Mathieu Bangert, PhD, Sanofi: Staff member Rolf Kramer, n/a, Sanofi: Stocks/Bonds John Paget, n/a, Sanofi Pasteur: Grant/Research Support

Turning radiology reports into epidemiological data to track seasonal pulmonary infections and the COVID-19 pandemic

ObjectivesTo automatically label chest radiographs and chest CTs regarding the detection of pulmonary infection in the report text, to calculate the number needed to image (NNI) and to investigate if these labels correlate with regional epidemiological infection data.Materials and methodsAll chest imaging reports performed in the emergency room between 01/2012 and 06/2022 were included (64,046 radiographs; 27,705 CTs). Using a regular expression-based text search algorithm, reports were labeled positive/negative for pulmonary infection if described.Data for regional weekly influenza-like illness (ILI) consultations (10/2013–3/2022), COVID-19 cases, and hospitalization (2/2020–6/2022) were matched with report labels based on calendar date. Positive rate for pulmonary infection detection, NNI, and the correlation with influenza/COVID-19 data were calculated.ResultsBetween 1/2012 and 2/2020, a 10.8–16.8% per year positive rate for detecting pulmonary infections on chest radiographs was found (NNI 6.0–9.3). A clear and significant seasonal change in mean monthly detection counts (102.3 winter; 61.5 summer; p < .001) correlated moderately with regional ILI consultations (weekly data r = 0.45; p < .001).For 2020–2021, monthly pulmonary infection counts detected by chest CT increased to 64–234 (23.0–26.7% per year positive rate, NNI 3.7–4.3) compared with 14–94 (22.4–26.7% positive rate, NNI 3.7–4.4) for 2012–2019. Regional COVID-19 epidemic waves correlated moderately with the positive pulmonary infection CT curve for 2020–2022 (weekly new cases: r = 0.53; hospitalizations: r = 0.65; p < .001).ConclusionText mining of radiology reports allows to automatically extract diagnoses. It provides a metric to calculate the number needed to image and to track the trend of diagnoses in real time, i.e., seasonality and epidemic course of pulmonary infections.Clinical relevanceDigitally labeling radiology reports represent previously neglected data and may assist in automated disease tracking, in the assessment of physicians’ clinical reasoning for ordering radiology examinations and serve as actionable data for hospital workflow optimization.Key Points• Radiology reports, commonly not machine readable, can be automatically labeled with the contained diagnoses using a regular-expression based text search algorithm.• Chest radiograph reports positive for pulmonary infection moderately correlated with regional influenza-like illness consultations (weekly data; r = 0.45; p < .001) and chest CT reports with the course of the regional COVID-19 pandemic (new cases: r = 0.53; hospitalizations: r = 0.65; p < 0.001).• Rendering radiology reports into data labels provides a metric for automated disease tracking, the assessment of ordering physicians clinical reasoning and can serve as actionable data for workflow optimization.

Coupling media coverage and susceptibility for modeling epidemic dynamics: An application to COVID-19

During an epidemic outbreak, social media is highly influential on human behavior and hence affects the spread of the epidemic course. In order to examine the impact of social media on disease transmission, a qualitative SIS-M type model with media-induced susceptibility is established, dealing with the amount of media coverage as an independent variable. The dynamic analysis of the proposed model is conducted theoretically and numerically. It is found that the growth rate of media coverage as well as the dissemination rate among the susceptible population may destabilize the endemic equilibrium under certain conditions, and consequently results in the existence of Hopf bifurcation. Moreover, the proposed model is applied to fit the daily confirmed cases of COVID-19 as well as the related news items for two cities in mainland China, and the estimated variation in daily confirmed cases triggered by the change of concerned parameters is investigated, revealing the effectiveness of our model and the performance of social media on mitigating the disease spread.

Epidemiological Implications of War: Machine Learning Estimations of the Russian Invasion’s Effect on Italy’s COVID-19 Dynamics

Background: The COVID-19 pandemic has profoundly transformed the global scenario, marked by overwhelming infections, fatalities, overburdened healthcare infrastructures, economic upheavals, and significant lifestyle modifications. Concurrently, the Russian full-scale invasion of Ukraine on 24 February 2022, triggered a severe humanitarian and public health crisis, leading to healthcare disruptions, medical resource shortages, and heightened emergency care needs. Italy emerged as a significant refuge for displaced Ukrainians during this period. Aim: This research aims to discern the impact of the Russian full-scale invasion of Ukraine on the COVID-19 transmission dynamics in Italy. Materials and Methods: The study employed advanced simulation methodologies, particularly those integrating machine learning, to model the pandemic’s trajectory. The XGBoost algorithm was adopted to construct a predictive model for the COVID-19 epidemic trajectory in Italy. Results: The model demonstrated a commendable accuracy of 86.03% in forecasting new COVID-19 cases in Italy over 30 days and an impressive 96.29% accuracy in estimating fatalities. When applied to the initial 30 days following the escalation of the conflict (24 February 2022, to 25 March 2022), the model’s projections suggested that the influx of Ukrainian refugees into Italy did not significantly alter the country’s COVID-19 epidemic course. Discussion: While simulation methodologies have been pivotal in the pandemic response, their accuracy is intrinsically linked to data quality, assumptions, and modeling techniques. Enhancing these methodologies can further their applicability in future public health emergencies. The findings from the model underscore that external geopolitical events, such as the mass migration from Ukraine, did not play a determinative role in Italy’s COVID-19 epidemic dynamics during the study period. Conclusion: The research provides empirical evidence negating a substantial influence of the Ukrainian refugee influx due to the Russian full-scale invasion on the COVID-19 epidemic trajectory in Italy. The robust performance of the developed model affirms its potential value in public health analyses.

Emerging themes and approaches in plant virus epidemiology.

Plant diseases caused by viruses share many common features with those caused by other pathogen taxa in terms of the host-pathogen interaction, but there are also distinctive features in epidemiology, most apparent where transmission is by vectors. Consequently, the host-virus-vector-environment interaction presents a continuing challenge in attempts to understand and predict the course of plant virus epidemics. Theoretical concepts, based on the underlying biology, can be expressed in mathematical models, and tested through quantitative assessments of epidemics in the field; this remains a goal in understanding why plant virus epidemics occur and how they can be controlled. To this end, this review identifies recent emerging themes and approaches to fill in knowledge gaps in plant virus epidemiology. We review quantitative work on impact of climatic fluctuations and change on plants, virus and vectors under different scenarios where impacts on the individual components of the plant-virus-vector interaction may vary disproportionately; the continuing sometimes discordant debate on host resistance and tolerance as plant defense mechanisms, including aspects of farmer behavior and attitudes to disease management that may affect deployment in crops; disentangling host-virus-vector-environment interactions as these contribute to temporal and spatial disease progress in field populations, computational techniques for estimating epidemiological parameters from field observations, and the use of optimal control analysis to assess disease control options. We end by proposing new challenges and questions in plant virus epidemiology.

Modeling the influence of the information domain on countermeasure effectiveness in case of COVID-19

A common way to model an epidemic — restricted to contagion aspects only — is a modification of the Kermack-McKendrick SIR Epidemic model (SIR model) with differential equations. (Mis-)Information about epidemics may influence the behavior of the people and thus the course of epidemics as well. We have thus coupled an extended SIR model of the COVID-19 pandemic with a compartment model of the (mis-)information-based attitude of the population towards epidemic countermeasures. The resulting combined model is checked concerning basic plausibility properties like positivity and boundedness. It is calibrated using COVID-19 data from RKI and attitude data provided by the COVID-19 Snapshot Monitoring (COSMO) study. The values of parameters without corresponding observation data have been determined using an L2 -fit under mild additional assumptions. The predictions of the calibrated model are essentially in accordance with observations. An uncertainty analysis of the model shows, that our results are in principle stable under measurement errors. We also assessed the scale, at which specific parameters can influence the evolution of epidemics. Another result of the paper is that in a multi-domain epidemic model, the notion of controlled reproduction number has to be redefined when being used as an indicator of the future evolution of epidemics.

Study on the COVID-19 epidemic in mainland China between November 2022 and January 2023, with prediction of its tendency

The prediction system EpiSIX was used to study the COVID-19 epidemic in mainland China between November 2022 and January 2023, based on reported data from December 9, 2022, to January 30, 2023, released by The Chinese Center for Disease Control and Prevention on February 1, 2023. Three kinds of reported data were used for model fitting: the daily numbers of positive nucleic acid tests and deaths, and the daily number of hospital beds taken by COVID-19 patients. It was estimated that the overall infection rate was 87.54% and the overall case fatality rate was 0.078%–0.116% (median 0.100%). Assuming that a new COVID-19 epidemic outbreak would start in March or April of 2023, induced by a slightly more infectious mutant strain, we predicted a possible large rebound between September and October 2023, with a peak demand of between 800,000 and 900,000 inpatient beds. If no such new outbreak was induced by other variants, then the current COVID-19 epidemic course in mainland China would remain under control until the end of 2023. However, it is suggested that the necessary medical resources be prepared to manage possible COVID-19 epidemic emergencies in the near future, especially for the period between September and October 2023.

Rapid evolution of a bacterial parasite during outbreaks in two Daphnia populations.

Myriad ecological and evolutionary factors can influence whether a particular parasite successfully transmits to a new host during a disease outbreak, with consequences for the structure and diversity of parasite populations. However, even though the diversity and evolution of parasite populations are of clear fundamental and applied importance, we have surprisingly few studies that track how genetic structure of parasites changes during naturally occurring outbreaks in non-human populations. Here, we used population genetic approaches to reveal how genotypes of a bacterial parasite, Pasteuria ramosa, change over time, focusing on how infecting P. ramosa genotypes change during the course of epidemics in Daphnia populations in two lakes. We found evidence for genetic change - and, therefore, evolution - of the parasite during outbreaks. In one lake, P. ramosa genotypes were structured by sampling date; in both lakes, genetic distance between groups of P. ramosa isolates increased with time between sampling. Diversity in parasite populations remained constant over epidemics, although one epidemic (which was large) had low genetic diversity while the other epidemic (which was small) had high genetic diversity. Our findings demonstrate that patterns of parasite evolution differ between outbreaks; future studies exploring the feedbacks among epidemic size, host diversity, and parasite genetic diversity would improve our understanding of parasite dynamics and evolution.

Intransigent vs. volatile opinions in a kinetic epidemic model with imitation game dynamics.

In the mathematical epidemiology community, there is an increasing interest in shaping the complex interplay between human behaviour and disease spreading. We give a contribution in this direction by illustrating a method to derive behavioural change epidemic models from a stochastic particle description by the means of kinetic equations. We consider a susceptible-infected-removed-like model where contact rates depend on the behavioural patterns adopted across the population. The selection of the social behaviour happens during the interactions between individuals adopting alternative strategies and it is driven by an imitation game dynamics. Agents have a double microscopic state: a discrete label, which denotes the epidemiological compartment to which they belong, and the degree of flexibility of opinion, i.e. a measure of the personal attitude to change opinion and, hence, to switch between the alternative social contact patterns. We derive kinetic evolution equations for the distribution functions of the degree of flexibility of opinion of the individuals for each compartment, whence we obtain macroscopic equations for the densities and average flexibilities of opinion. After providing the basic properties of the macroscopic model, we numerically investigate it by focusing on the impact of the flexibility of opinion on the epidemic course and on the consequent behavioural responses.

Dynamics of SARS-CoV-2 lineages in French Guiana in 2020-2021: 4 epidemic waves with cross-influences from Europe and South America.

Since the first cases of SARS-CoV-2 infection in Wuhan in December 2019, this RNA virus gave rise to different viral lineages with different virological, epidemiological and immunological properties. Here we describe the dynamics of circulation of SARS-CoV-2 lineages in an Amazonian South American French overseas territory, French Guiana (FG). The data analyzed are based on the general epidemic course, and genomic surveillance data come from whole genome sequencing (WGS) as well as typing PCRs. From March 2020 to October 2021, four COVID-19 epidemic waves were observed in FG with an evolution of viral lineages influenced by virus introductions from continental France and above all by land-based introductions from neighbouring countries. The third epidemic wave from March to June 2021 was driven by a predominant Gamma introduced from Brazil and a less frequent Alpha introduced from France. This coexistence was completely substituted by Delta that initiated the fourth epidemic wave.

Mobility was a significant determinant of reported COVID-19 incidence during the Omicron Surge in the most populous U.S. Counties

BackgroundSignificant immune escape by the Omicron variant, along with the emergence of widespread worry fatigue, have called into question the robustness of the previously observed relation between population mobility and COVID-19 incidence.MethodsWe employed principal component analysis to construct a one-dimensional summary indicator of six Google mobility categories. We related this mobility indicator to case incidence among 111 of the most populous U.S. counties during the Omicron surge from December 2021 through February 2022.ResultsReported COVID-19 incidence peaked earlier and declined more rapidly among those counties exhibiting more extensive decline in mobility between December 20 and January 3. Based upon a fixed-effects, longitudinal cohort model, we estimated that every 1% decline in mobility between December 20 and January 3 was associated with a 0.63% decline in peak incidence during the week ending January 17 (95% confidence interval, 0.40–0.86%). Based upon a cross-sectional analysis including mean household size and vaccination participation as covariates, we estimated that the same 1% decline in mobility was associated with a 0.36% decline in cumulative reported COVID-19 incidence from January 10 through February 28 (95% CI, 0.18–0.54%).ConclusionOmicron did not simply sweep through the U.S. population until it ran out of susceptible individuals to infect. To the contrary, a significant fraction managed to avoid infection by engaging in risk-mitigating behaviors. More broadly, the behavioral response to perceived risk should be viewed as an intrinsic component of the natural course of epidemics in humans.

Bias and bias-correction for individual-level models of infectious disease

Accurate infectious disease models can help scientists understand how an ongoing disease epidemic spreads and forecast the course of epidemics more effectively. Considering various factors that affect the spread of a disease (e.g. geographical, social, domestic, and genetic), a class of individual-level models (ILMs) was developed to incorporate population heterogeneity. In these models, inferences are developed within a Bayesian Markov chain Monte Carlo (MCMC) framework, obtaining posterior estimates of model parameters. The issues of bias of parameter estimates, and methods for bias correction, have been widely studied with respect to many of the most established and commonly used statistical models and associated methods of parameter estimation. However, these methods are not directly applicable to infectious disease data. This paper investigates circumstances in which ILM parameter estimates may be biased in some simple disease system scenarios. Further, we aim to compare the performance of bias-corrected estimates of ILM parameters, using simulation, with the posterior estimates of the parameter. We also discuss the factors that affect the performance of these estimators.

Epidemic trend analysis of SARS-CoV-2 in South Asian Association for Regional Cooperation countries using modified susceptible-infected-recovered predictive model.

A novel coronavirus causing the severe and fatal respiratory syndrome was identified in China, is now producing outbreaks in more than 200 countries around the world, and became pandemic by the time. In this article, a modified version of the well‐known mathematical epidemic model susceptible‐infected‐recovered (SIR) is used to analyze the epidemic's course of COVID‐19 in eight different countries of the South Asian Association for Regional Cooperation (SAARC). To achieve this goal, the parameters of the SIR model are identified by using publicly available data for the corresponding countries: Afghanistan, Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan, and Sri Lanka. Based on the prediction model, we estimated the epidemic trend of COVID‐19 outbreak in SAARC countries for 20, 90, and 180 days, respectively. A short‐mid‐long term prediction model has been designed to understand the early dynamics of the COVID‐19 epidemic in the southeast Asian region. The maximum and minimum basic reproduction numbers (R 0 = 1.33 and 1.07) for SAARC countries are predicted to be in Pakistan and Bhutan. We equate simulation results with real data in the SAARC countries on the COVID‐19 outbreak, and predicted different scenarios using the modified SIR prediction model. Our results should provide policymakers with a method for evaluating the impacts of possible interventions, including lockdown and social distancing, as well as testing and contact tracking.

COVID-19 pandemic and its impact on other infections in Northwest Russia

The purpose statement is to assess the course of the COVID-19 pandemic and its impact on the epidemics of HIV infection, viral hepatitis C (HCV), tuberculosis, influenza, and acute respiratory infections in the North-Western Federal District (NWFD) of the Russian Federation during two years of spreading COVID-19 from the standpoint of the possible formation of syndemia or interference between pathogens.Materials and methods. Data collection on HIV infection and HCV was carried out by Pasteur Research Institute of Epidemiology and Microbiology at St. Petersburg Medical Information and Analytical Center using data from federal state statistical observation forms and our own research materials. The indicators of excess mortality from all causes for 2020 in the Northwestern Federal District (NWFD) and in the Russian Federation were analyzed according to the data of the Federal State Statistics Service (ROSSTAT). The results of epidemiological surveillance of HIV infection, viral hepatitis C (HCV), and tuberculosis in the Russian Federation and in the NWED in 2020 and 2021 in comparison with previous years are summarized. Studies of influenza and SARS were carried out by Smorodintsev Research Institute of Influenza and Gamaleya National Research Center for Epidemiology and Microbiology in cooperation with regional support bases. PCR detection of ARVI pathogens was carried out.Results and discussion. An analysis of epidemic indicators across the territories of the Northwestern Federal District of the Russian Federation made it possible to identify the following features: heterogeneity of the incidence of COVID-19 in the population in terms of time and intensity; the prevalence of morbidity in the metropolis (St. Petersburg) and industrial northern regions (the republics of Karelia and Komi). In a comparative aspect, the NWFD belongs to regions with high morbidity and mortality in the Russian Federation. St. Petersburg (378 deaths per 100,000 people in 2021) ranks second after Moscow. In turn, the Vologda, Murmansk, and Arkhangelsk regions, as well as the Komi Republic are among the top ten of the most affected subjects of the Russian Federation in the country. In terms of mortality, St. Petersburg was significantly ahead of all other subjects of the Russian Federation and the national average, which had several probable reasons. The first and second years of the pandemic did not reveal significant changes in epidemic rates of morbidity and mortality from other socially significant infections — HIV, HCV, and tuberculosis at the population level characteristic interference of viruses, where SARS-CoV-2 took the place of interfering, and influenza and SARS pretending or interfering pathogens. It took several months until the beginning of winter 2020 to crowd out seasonal respiratory infections by SARS-CoV-2. Then SARS-CoV-2 tightly captured the epidemic space, leaving no room for traditional respiratory infections, which accounted for less than 5% in early 2022.Conclusion. The COVID-19 epidemic had pronounced developmental features with higher morbidity and mortality in the metropolis and other industrial centers. The pandemic did not significantly affect the patterns of the epidemic course of HIV, HCV, and tuberculosis, which rejected the presence of a syndemic between these pathogens. Interference with respiratory infections — influenza and SARS — was revealed with a clear predominance of SARS-CoV-2.