Changing spatiotemporal patterns for hepatitis of unspecified aetiology in China, 2004-2021: a population-based surveillance study.

Objectives: As global efforts continue toward the target of eliminating viral hepatitis by 2030, the emergence of acute hepatitis of unspecified aetiology (HUA) remains a concern. This study assesses the overall trends and changes in spatiotemporal patterns in HUA in China from 2004 to 2021. Methods: We extracted the incidence and mortality rates of HUA from the Public Health Data Center, the official website of the National Health Commission of the People's Republic of China, and the National Notifiable Infectious Disease Surveillance System from 2004 to 2021. We used R software, ArcGIS, Moran's statistical analysis, and joinpoint regression to examine the spatiotemporal patterns and annual percentage change in incidence and mortality of the HUA across China. Results: From 2004 to 2021, a total of 707,559 cases of HUA have been diagnosed, including 636 deaths. The proportion of HUA in viral hepatitis gradually decreased from 7.55% in 2004 to 0.72% in 2021. The annual incidence of HUA decreased sharply from 6.6957 per 100,000 population in 2004 to 0.6302 per 100,000 population in 2021, with an average annual percentage change (APC) reduction of -13.1% (p < 0.001). The same result was seen in the mortality (APC, -22.14%, from 0.0089/100,000 in 2004 to 0.0002/100,000 in 2021, p < 0.001). All Chinese provinces saw a decline in incidence and mortality. Longitudinal analysis identified the age distribution in the incidence and mortality of HUA did not change and was highest in persons aged 15-59 years, accounting for 70% of all reported cases. During the COVID-19 pandemic, no significant increase was seen in pediatric HUA cases in China. Conclusion: China is experiencing an unprecedented decline in HUA, with the lowest incidence and mortality for 18 years. However, it is still important to sensitively monitor the overall trends of HUA and further improve HUA public health policy and practice in China.

Impacts of observation frequency on proximity contact data and modeled transmission dynamics.

Transmission of many communicable diseases depends on proximity contacts among humans. Modeling the dynamics of proximity contacts can help determine whether an outbreak is likely to trigger an epidemic. While the advent of commodity mobile devices has eased the collection of proximity contact data, battery capacity and associated costs impose tradeoffs between the observation frequency and scanning duration used for contact detection. The choice of observation frequency should depend on the characteristics of a particular pathogen and accompanying disease. We downsampled data from five contact network studies, each measuring participant-participant contact every 5 minutes for durations of four or more weeks. These studies included a total of 284 participants and exhibited different community structures. We found that for epidemiological models employing high-resolution proximity data, both the observation method and observation frequency configured to collect proximity data impact the simulation results. This impact is subject to the population's characteristics as well as pathogen infectiousness. By comparing the performance of two observation methods, we found that in most cases, half-hourly Bluetooth discovery for one minute can collect proximity data that allows agent-based transmission models to produce a reasonable estimation of the attack rate, but more frequent Bluetooth discovery is preferred to model individual infection risks or for highly transmissible pathogens. Our findings inform the empirical basis for guidelines to inform data collection that is both efficient and effective.

Salmonellosis in Australia in 2020: possible impacts of COVID-19 related public health measures.

BACKGROUND: More than seventy per cent of salmonellosis in Australia is thought to be due to contaminated food. Rates of salmonellosis vary across the Australian states and territories, with the highest rates in the Northern Territory. In 2020, to control coronavirus disease 2019 (COVID-19), Australia implemented public health measures including border closures, physical distancing and hygiene advice. This study analyses salmonellosis notification rates in 2020 and considers possible impacts of COVID-19 measures. METHODS: Monthly and annual salmonellosis notifications per 100,000 population, for each of Australia's eight states and territories for the years 2015 to 2020, were extracted from Australia's publicly accessible National Notifiable Diseases Surveillance System. For each jurisdiction, the salmonellosis rate each month in 2020 was compared with the previous 5-year median rate for that calendar month. The possible impacts of COVID-19 public health measures on salmonellosis notifications in the respective states and territories were examined. RESULTS: The annual Australian salmonellosis notification rate was 27% lower in 2020 than the previous 5-year median. The reduction in salmonellosis rate varied throughout Australia. States and territories with more stringent, more frequent or longer COVID-19 public health measures had generally greater salmonellosis rate reductions. However, Tasmania had a 50% deeper reduction in salmonellosis rate than did the Northern Territory, despite similar restriction levels. CONCLUSIONS: Salmonellosis notifications decreased in Australia during the global COVID-19 pandemic. The reduction in notifications corresponded with the implementation of public health measures. Persistence of high rates in the Northern Territory could indicate the overarching importance of demographic and environmental factors.

Perceptive movement of susceptible individuals with memory.

The perception of susceptible individuals naturally lowers the transmission probability of an infectious disease but has been often ignored. In this paper, we formulate and analyze a diffusive SIS epidemic model with memory-based perceptive movement, where the perceptive movement describes a strategy for susceptible individuals to escape from infections. We prove the global existence and boundedness of a classical solution in an n-dimensional bounded smooth domain. We show the threshold-type dynamics in terms of the basic reproduction number [Formula: see text]: when [Formula: see text], the unique disease-free equilibrium is globally asymptotically stable; when [Formula: see text], there is a unique constant endemic equilibrium, and the model is uniformly persistent. Numerical analysis exhibits that when [Formula: see text], solutions converge to the endemic equilibrium for slow memory-based movement and they converge to a stable periodic solution when memory-based movement is fast. Our results imply that the memory-based movement cannot determine the extinction or persistence of infectious disease, but it can change the persistence manner.

Assessing the accuracy of California county level COVID-19 hospitalization forecasts to inform public policy decision making.

BACKGROUND: The COVID-19 pandemic has highlighted the role of infectious disease forecasting in informing public policy. However, significant barriers remain for effectively linking infectious disease forecasts to public health decision making, including a lack of model validation. Forecasting model performance and accuracy should be evaluated retrospectively to understand under which conditions models were reliable and could be improved in the future. METHODS: Using archived forecasts from the California Department of Public Health's California COVID Assessment Tool ( https://calcat.covid19.ca.gov/cacovidmodels/ ), we compared how well different forecasting models predicted COVID-19 hospitalization census across California counties and regions during periods of Alpha, Delta, and Omicron variant predominance. RESULTS: Based on mean absolute error estimates, forecasting models had variable performance across counties and through time. When accounting for model availability across counties and dates, some individual models performed consistently better than the ensemble model, but model rankings still differed across counties. Local transmission trends, variant prevalence, and county population size were informative predictors for determining which model performed best for a given county based on a random forest classification analysis. Overall, the ensemble model performed worse in less populous counties, in part because of fewer model contributors in these locations. CONCLUSIONS: Ensemble model predictions could be improved by incorporating geographic heterogeneity in model coverage and performance. Consistency in model reporting and improved model validation can strengthen the role of infectious disease forecasting in real-time public health decision making.

Unveiling the paths of COVID-19 in a large city based on public transportation data.

Human mobility plays a key role in the dissemination of infectious diseases around the world. However, the complexity introduced by commuting patterns in the daily life of cities makes such a role unclear, especially at the intracity scale. Here, we propose a multiplex network fed with 9 months of mobility data with more than 107 million public bus validations in order to understand the relation between urban mobility and the spreading of COVID-19 within a large city, namely, Fortaleza in the northeast of Brazil. Our results suggest that the shortest bus rides in Fortaleza, measured in the number of daily rides among all neighborhoods, decreased [Formula: see text]% more than the longest ones after an epidemic wave. Such a result is the opposite of what has been observed at the intercity scale. We also find that mobility changes among the neighborhoods are synchronous and geographically homogeneous. Furthermore, we find that the most central neighborhoods in mobility are the first targets for infectious disease outbreaks, which is quantified here in terms of the positive linear relation between the disease arrival time and the average of the closeness centrality ranking. These central neighborhoods are also the top neighborhoods in the number of reported cases at the end of an epidemic wave as indicated by the exponential decay behavior of the disease arrival time in relation to the number of accumulated reported cases with decay constant [Formula: see text] days. We believe that these results can help in the development of new strategies to impose restriction measures in the cities guiding decision-makers with smart actions in public health policies, as well as supporting future research on urban mobility and epidemiology.

[Influence of the COVID-19 pandemic on the occurrence of neurological infectious diseases]. / Einfluss der COVID-19-Pandemie auf das Auftreten neuroinfektiologischer Erkrankungen.

BACKGROUND: During the coronavirus disease 2019 (COVID-19) pandemic a wide range of hygiene measures were implemented to contain the spread of infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Besides a mitigation of SARS-CoV­2, a decline in the number of other respiratory tract infections could be observed. Interestingly, the numbers for some infections of the central nervous system (CNS) decreased as well. OBJECTIVE: This review article shows the development of important CNS infections in Germany during the COVID-19 pandemic. MATERIAL AND METHOD: This article is based on relevant literature on the epidemiology of CNS infections during the COVID-19 pandemic up to autumn 2022. RESULTS: During the COVID-19 pandemic the frequency of bacterial meningitis caused by Streptococcus pneumoniae, Neisseria meningitidis and Haemophilus influenzae significantly declined. The frequency of viral meningitis, particularly those caused by Enterovirus, decreased as well. In contrast, the number of patients suffering from tick-borne encephalitis significantly increased within the first year of the pandemic. DISCUSSION: During the pandemic there was a decrease in cases of bacterial and viral meningitis, most likely due to the general containment strategies and social contact restrictions. The increase of infections transmitted by ticks could be a consequence of changed leisure activities during the pandemic.

An implementation framework to improve the transparency and reproducibility of computational models of infectious diseases.

Computational models of infectious diseases have become valuable tools for research and the public health response against epidemic threats. The reproducibility of computational models has been limited, undermining the scientific process and possibly trust in modeling results and related response strategies, such as vaccination. We translated published reproducibility guidelines from a wide range of scientific disciplines into an implementation framework for improving reproducibility of infectious disease computational models. The framework comprises 22 elements that should be described, grouped into 6 categories: computational environment, analytical software, model description, model implementation, data, and experimental protocol. The framework can be used by scientific communities to develop actionable tools for sharing computational models in a reproducible way.

Vaccination of Ukrainian Refugees: Need for Urgent Action.

The unprovoked aggression of Russian military forces on Ukraine in February 2022 has caused a high influx of refugees, including children, to neighboring countries, particularly Poland. This caused additional pressures on the healthcare system and the need to meet challenges for public health, such as those related to infectious diseases. Here, we discuss the potential epidemiological risks associated with the war-induced influx of refugees (coronavirus disease 2019, measles, pertussis, tetanus, and poliomyelitis) and highlight the need for their swift management through institutional support, educational campaigns, counteracting antiscience misinformation, and pursuing vaccinations of refugees but also improving or maintaining good levels of immunization in populations of countries welcoming them. These are necessary actions to avoid overlapping of war and infectious diseases and associated public health challenges.

Cumulative damage for multi-type epidemics and an application to infectious diseases.

A continuous time multivariate stochastic model is proposed for assessing the damage of a multi-type epidemic cause to a population as it unfolds. The instants when cases occur and the magnitude of their injure are random. Thus, we define a cumulative damage based on counting processes and a multivariate mark process. For a large population we approximate the behavior of this damage process by its asymptotic distribution. Also, we analyze the distribution of the stopping times when the numbers of cases caused by the epidemic attain levels beyond certain thresholds. We focus on introducing some tools for statistical inference on the parameters related with the epidemic. In this regard, we present a general hypothesis test for homogeneity in epidemics and apply it to data of Covid-19 in Chile.

Usefulness of linked data for infectious disease events: a systematic review.

Surveillance is a key public health function to enable early detection of infectious disease events and inform public health action. Data linkage may improve the depth of data for response to infectious disease events. This study aimed to describe the uses of linked data for infectious disease events. A systematic review was conducted using Pubmed, CINAHL and Web of Science. Studies were included if they used data linkage for an acute infectious disease event (e.g. outbreak of disease). We summarised the event, study aims and designs; data sets; linkage methods; outcomes reported; and benefits and limitations. Fifty-four studies were included. Uses of linkage for infectious disease events included assessment of severity of disease and risk factors; improved case finding and contact tracing; and vaccine uptake, safety and effectiveness. The ability to conduct larger scale population level studies was identified as a benefit, in particular for rarer exposures, risk factors or outcomes. Limitations included timeliness, data quality and inability to collect additional variables. This review demonstrated multiple uses of data linkage for infectious disease events. As infectious disease events occur without warning, there is a need to establish pre-approved protocols and the infrastructure for data-linkage to enhance information available during an event.

Infectious diseases in Poland in 2020.

OBJECTIVE OF THE WORK: The article reviews the main problems of the epidemiology of infectious diseases in Poland in 2020. It summarizes relevant findings from the national infectious disease surveillance system. MATERIAL AND METHODS: The data contained in this article come from the reports collected by the State Sanitary Inspection on cases of notifiable infectious diseases notified by clinicians and/or laboratories. These are supplemented by mortality data published by the Statistics Poland. RESULTS AND THEIR DISCUSSION: The epidemiology of infectious diseases was highly impacted by the COVID-19 pandemic. There were 1,306,983 cases notified in 2020 and 41,451 deaths attributed to COVID-19 (according to Statistics Poland). The reported incidence of other infections decreased by 10-98%. We noted especially high decreases in the incidence of viral gastrointestinal infections (by over 70%). The incidence of influenza and influenza-like infections decreased by 34% and tuberculosis by 36% as compared to 2019. However, important decreases were also noted for other diseases under surveillance, which could point to disruption of diagnosis services and reporting due to lockdowns and high workload on the public health services.

Global analysis and prediction scenario of infectious outbreaks by recurrent dynamic model and machine learning models: A case study on COVID-19.

It is essential to evaluate patient outcomes at an early stage when dealing with a pandemic to provide optimal clinical care and resource management. Many methods have been proposed to provide a roadmap against different pandemics, including the recent pandemic disease COVID-19. Due to recurrent epidemic waves of COVID-19, which have been observed in many countries, mathematical modeling and forecasting of COVID-19 are still necessary as long as the world continues to battle against the pandemic. Modeling may aid in determining which interventions to try or predict future growth patterns. In this article, we design a combined approach for analyzing any pandemic in two separate parts. In the first part of the paper, we develop a recurrent SEIRS compartmental model to predict recurrent outbreak patterns of diseases. Due to its time-varying parameters, our model is able to reflect the dynamics of infectious diseases, and to measure the effectiveness of the restrictive measures. We discuss the stable solutions of the corresponding autonomous system with frozen parameters. We focus on the regime shifts and tipping points; then we investigate tipping phenomena due to parameter drifts in our time-varying parameters model that exhibits a bifurcation in the frozen-in case. Furthermore, we propose an optimal numerical design for estimating the system's parameters. In the second part, we introduce machine learning models to strengthen the methodology of our paper in data analysis, particularly for prediction scenarios. We use MLP, RBF, LSTM, ANFIS, and GRNN for training and evaluation of COVID-19. Then, we compare the results with the recurrent dynamical system in the fitting process and prediction scenario. We also confirm results by implementing our methods on the released data on COVID-19 by WHO for Italy, Germany, Iran, and South Africa between 1/22/2020 and 7/24/2021, when people were engaged with different variants including Alpha, Beta, Gamma, and Delta. The results of this article show that the dynamic model is adequate for long-term analysis and data fitting, as well as obtaining parameters affecting the epidemic. However, it is ineffective in providing a long-term forecast. In contrast machine learning methods effectively provide disease prediction, although they do not provide analysis such as dynamic models. Finally, some metrics, including RMSE, R-Squared, and accuracy, are used to evaluate the machine learning models. These metrics confirm that ANFIS and RBF perform better than other methods in training and testing zones.

Surveillance of communicable diseases using social media: A systematic review.

BACKGROUND: Communicable diseases pose a severe threat to public health and economic growth. The traditional methods that are used for public health surveillance, however, involve many drawbacks, such as being labor intensive to operate and resulting in a lag between data collection and reporting. To effectively address the limitations of these traditional methods and to mitigate the adverse effects of these diseases, a proactive and real-time public health surveillance system is needed. Previous studies have indicated the usefulness of performing text mining on social media. OBJECTIVE: To conduct a systematic review of the literature that used textual content published to social media for the purpose of the surveillance and prediction of communicable diseases. METHODOLOGY: Broad search queries were formulated and performed in four databases. Both journal articles and conference materials were included. The quality of the studies, operationalized as reliability and validity, was assessed. This qualitative systematic review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS: Twenty-three publications were included in this systematic review. All studies reported positive results for using textual social media content to surveille communicable diseases. Most studies used Twitter as a source for these data. Influenza was studied most frequently, while other communicable diseases received far less attention. Journal articles had a higher quality (reliability and validity) than conference papers. However, studies often failed to provide important information about procedures and implementation. CONCLUSION: Text mining of health-related content published on social media can serve as a novel and powerful tool for the automated, real-time, and remote monitoring of public health and for the surveillance and prediction of communicable diseases in particular. This tool can address limitations related to traditional surveillance methods, and it has the potential to supplement traditional methods for public health surveillance.

Non-pharmaceutical interventions for COVID-19 reduced the incidence of infectious diseases: a controlled interrupted time-series study.

BACKGROUND: Non-pharmaceutical interventions (NPIs) have been implemented worldwide to suppress the spread of coronavirus disease 2019 (COVID-19). However, few studies have evaluated the effect of NPIs on other infectious diseases and none has assessed the avoided disease burden associated with NPIs. We aimed to assess the effect of NPIs on the incidence of infectious diseases during the COVID-19 pandemic in 2020 and evaluate the health economic benefits related to the reduction in the incidence of infectious diseases. METHODS: Data on 10 notifiable infectious diseases across China during 2010-2020 were extracted from the China Information System for Disease Control and Prevention. A two-stage controlled interrupted time-series design with a quasi-Poisson regression model was used to examine the impact of NPIs on the incidence of infectious diseases. The analysis was first performed at the provincial-level administrative divisions (PLADs) level in China, then the PLAD-specific estimates were pooled using a random-effect meta-analysis. RESULTS: A total of 61,393,737 cases of 10 infectious diseases were identified. The implementation of NPIs was associated with 5.13 million (95% confidence interval [CI] 3.45‒7.42) avoided cases and USD 1.77 billion (95% CI 1.18‒2.57) avoided hospital expenditures in 2020. There were 4.52 million (95% CI 3.00‒6.63) avoided cases for children and adolescents, corresponding to 88.2% of total avoided cases. The top leading cause of avoided burden attributable to NPIs was influenza [avoided percentage (AP): 89.3%; 95% CI 84.5‒92.6]. Socioeconomic status and population density were effect modifiers. CONCLUSIONS: NPIs for COVID-19 could effectively control the prevalence of infectious diseases, with patterns of risk varying by socioeconomic status. These findings have important implications for informing targeted strategies to prevent infectious diseases.

Salmonellosis in Poland in 2020.

AIM: The aim of the study is to assess the epidemiological situation of salmonellosis in Poland in 2020 compared with previous years. MATERIAL AND METHODS: The epidemiological situation was assessed on the basis of data provided to the Department of Epidemiology of Infectious Diseases and Surveillance of the NIPH NIH-NRI by sanitary-epidemiological stations through the EpiBaza System and the Registry of Epidemic Outbreaks System (ROE), as well as on the basis of data published in the annual bulletin "Infectious Diseases and Poisoning in Poland in 2020" (NIPH NIH-NRI, GIS, Warsaw, 2021) and from information received from laboratories of sanitary-epidemiological stations and data from the Demographic Research Department of the Statistics Poland. RESULTS: In Poland in 2020, in the sanitary-epidemiological surveillance registered a total of 5,470 cases of salmonellosis, 5,302 cases of intestinal salmonellosis, and the remaining 168 cases of extra-intestinal salmonellosis. The incidence per 100,000 population was 14.3 for total salmonellosis, 13.8 for intestinal salmonellosis and 0.44 for extra-intestinal salmonellosis. Sanitary-epidemiological stations registered 5,349 confirmed cases and 121 probable cases of salmonellosis. Due to intestinal salmonellosis, 63.9% of all patients were hospitalized, while for extra-intestinal salmonellosis 153 patients or 91.1% of cases, were hospitalized. The increase in the number of salmonellosis cases in 2020 started in June, while the peak of the incidence was in August. Among the voivodeships, the highest incidence of salmonellosis was registered in the Podkarpackie voivodeship 33.3/100,000 population, the lowest in Zachodniopomorskie 6.1/100,000 population. Cases in the 0-4 age group accounted for 45.2% of all salmonellosis cases in 2020. Among extra-intestinal salmonellosis, 63.1% were people aged 60+. Sanitary-epidemiological stations registered 131 food poisoning outbreaks caused by Salmonella bacilli in the ROE system, 108 of these outbreaks were caused by the Enteritidis serotype. In 2020, the most common serotypes were S. Enteritidis 70% of all recorded salmonellosis, S. Typhimurium 1.9%, and S. Infantis 0.54%. There were 9 deaths due to Salmonella infection. CONCLUSIONS: The COVID-19 pandemic and the associated restrictions introduced in the country, as well as increased hygiene through more frequent washing and disinfection of hands, could have contributed to a reduction of almost 69% in the number of salmonellosis cases registered in 2020, in Poland, compared to 2019. This is a 82% decrease in relation to 2018. There was also a decrease in the number of food poisoning outbreaks caused by Salmonella bacilli, while at the same time their percentage in the total number of outbreaks increased. On the one hand, the implemented restrictions could have had an impact on the decrease in the number of cases and outbreaks, on the other hand, worse access to medical care and diagnostics, most likely deepened the underestimation of these cases in the country observed for years, and distorted the real picture of the situation.

Endemic, epidemic and pandemic infections: the roles of natural and acquired herd immunity.

PURPOSE OF REVIEW: This review summarizes the general concepts of innate and acquired immunity, including vaccine use and hesitancy, as they relate to reduction of the global burden of highly communicable infectious diseases. RECENT FINDINGS: Vaccination to increase herd immunity remains the cornerstone of disease prevention worldwide yet global vaccination goals are not being met. Modern obstacles to vaccine acceptance include hesitancy, reduced altruistic intentions, impact of COVID-19, distrust of science and governmental agencies as well as recent geopolitical and environmental disasters. Together, such barriers have negatively impacted immunization rates worldwide, resulting in epidemics and pandemics of serious life-threatening infections from vaccine-preventable diseases, especially those affecting children. In addition, pathogens thought to be controlled or eradicated are reemerging with new genetic traits, making them more able to evade natural and acquired immunity, including that induced by available vaccines. Lastly, many serious and widespread infectious diseases await development and utilization of efficacious vaccines. SUMMARY: The global burden of communicable diseases remains high, necessitating continued pathogen surveillance as well as vaccine development, deployment and continued efficacy testing. Equally important is the need to educate aggressively the people and their leaders on the benefits of vaccination to the individual, local community and the human population as a whole.