Quantifying optimal resource allocation strategies for controlling epidemics.

Frequent emergence of communicable diseases is a major concern worldwide. Lack of sufficient resources to mitigate the disease burden makes the situation even more challenging for lower-income countries. Hence, strategy development for disease eradication and optimal management of the social and economic burden has garnered a lot of attention in recent years. In this context, we quantify the optimal fraction of resources that can be allocated to two major intervention measures, namely reduction of disease transmission and improvement of healthcare infrastructure. Our results demonstrate that the effectiveness of each of the interventions has a significant impact on the optimal resource allocation in both long-term disease dynamics and outbreak scenarios. The optimal allocation strategy for long-term dynamics exhibits non-monotonic behaviour with respect to the effectiveness of interventions, which differs from the more intuitive strategy recommended in the case of outbreaks. Further, our results indicate that the relationship between investment in interventions and the corresponding increase in patient recovery rate or decrease in disease transmission rate plays a decisive role in determining optimal strategies. Intervention programmes with decreasing returns promote the necessity for resource sharing. Our study provides fundamental insights into determining the best response strategy when controlling epidemics in resource-constrained situations.

Assessing the heterogeneity in the transmission of infectious diseases from time series of epidemiological data.

Structural features and the heterogeneity of disease transmissions play an essential role in the dynamics of epidemic spread. But these aspects can not completely be assessed from aggregate data or macroscopic indicators such as the effective reproduction number. We propose in this paper an index of effective aggregate dispersion (EffDI) that indicates the significance of infection clusters and superspreading events in the progression of outbreaks by carefully measuring the level of relative stochasticity in time series of reported case numbers using a specially crafted statistical model for reproduction. This allows to detect potential transitions from predominantly clustered spreading to a diffusive regime with diminishing significance of singular clusters, which can be a decisive turning point in the progression of outbreaks and relevant in the planning of containment measures. We evaluate EffDI for SARS-CoV-2 case data in different countries and compare the results with a quantifier for the socio-demographic heterogeneity in disease transmissions in a case study to substantiate that EffDI qualifies as a measure for the heterogeneity in transmission dynamics.

Editorial: Infectious Disease Surveillance Using Artificial Intelligence (AI) and its Role in Epidemic and Pandemic Preparedness.

Artificial intelligence (AI), or machine learning, is an ancient concept based on the assumption that human thought and reasoning can be mechanized. AI techniques have been used in diagnostic medicine for several decades, particularly in image analysis and clinical diagnosis. During the COVID-19 pandemic, AI was critical in genome sequencing, drug and vaccine development, identifying disease outbreaks, monitoring disease spread, and tracking viral variants. AI-driven approaches complement human-curated ones, including traditional public health surveillance. Preparation for future pandemics will require the combined efforts of collaborative surveillance networks, which currently include the US Centers for Disease Control and Prevention (CDC) Center for Forecasting and Outbreak Analytics and the World Health Organization (WHO) Hub for Pandemic and Epidemic Intelligence, which will use AI combined with international cooperation to implement AI in surveillance programs. This Editorial aims to provide an update on the uses and limitations of AI in infectious disease surveillance and pandemic preparedness.

The impact of pandemic mental cognition on cultural values: an empirical study based on social media.

BACKGROUND: COVID-19 has triggered a global public health crisis, and had an impact on economies, societies, and politics around the world. Based on the pathogen prevalence hypothesis suggested that residents of areas with higher infection rates are more likely to be collectivists as compared with those of areas with lower infection rates. Many researchers had studied the direct link between infectious diseases and individualism/collectivism (infectious diseases→ cultural values), but no one has focused on the specific psychological factors between them: (infectious diseases→ cognition of the pandemic→ cultural values). To test and develop the pathogen prevalence hypothesis, we introduced pandemic mental cognition and conducted an empirical study on social media (Chinese Sina Weibo), hoping to explore the psychological reasons behind in cultural value changes in the context of a pandemic. METHODS: We downloaded all posts from active Sina Weibo users in Dalian during the pandemic period (January 2020 to May 2022) and used dictionary-based approaches to calculate frequency of words from two domains (pandemic mental cognition and collectivism/individualism), respectively. Then we used the multiple log-linear regression analysis method to establish the relationship between pandemic mental cognition and collectivism/individualism. RESULTS: Among three dimensions of pandemic mental cognition, only the sense of uncertainty had a significant positive relationship with collectivism, and also had a marginal significant positive relationship with individualism. There was a significant positive correlation between the first-order lag term AR(1) and individualism, which means the individualism tendency was mainly affected by its previous level. CONCLUSIONS: The study found that more collectivist regions are associated with a higher pathogen burden, and recognized the sense of uncertainty as its underlying cause. Results of this study validated and further developed the pathogen stress hypothesis in the context of the COVID-19 pandemic.

The indirect impacts of nonpharmacological COVID-19 control measures on other infectious diseases in Yinchuan, Northwest China: a time series study.

BACKGROUND: Various nonpharmaceutical interventions (NPIs) against COVID-19 continue to have an impact on socioeconomic and population behaviour patterns. However, the effect of NPIs on notifiable infectious diseases remains inconclusive due to the variability of the disease spectrum, high-incidence endemic diseases and environmental factors across different geographical regions. Thus, it is of public health interest to explore the influence of NPIs on notifiable infectious diseases in Yinchuan, Northwest China. METHODS: Based on data on notifiable infectious diseases (NIDs), air pollutants, meteorological data, and the number of health institutional personnel in Yinchuan, we first fitted dynamic regression time series models to the incidence of NIDs from 2013 to 2019 and then estimated the incidence for 2020. Then, we compared the projected time series data with the observed incidence of NIDs in 2020. We calculated the relative reduction in NIDs at different emergency response levels in 2020 to identify the impacts of NIPs on NIDs in Yinchuan. RESULTS: A total of 15,711 cases of NIDs were reported in Yinchuan in 2020, which was 42.59% lower than the average annual number of cases from 2013 to 2019. Natural focal diseases and vector-borne infectious diseases showed an increasing trend, as the observed incidence in 2020 was 46.86% higher than the estimated cases. The observed number of cases changed in respiratory infectious diseases, intestinal infectious diseases and sexually transmitted or bloodborne diseases were 65.27%, 58.45% and 35.01% higher than the expected number, respectively. The NIDs with the highest reductions in each subgroup were hand, foot, and mouth disease (5854 cases), infectious diarrhoea (2157 cases) and scarlet fever (832 cases), respectively. In addition, it was also found that the expected relative reduction in NIDs in 2020 showed a decline across different emergency response levels, as the relative reduction dropped from 65.65% (95% CI: -65.86%, 80.84%) during the level 1 response to 52.72% (95% CI: 20.84%, 66.30%) during the level 3 response. CONCLUSIONS: The widespread implementation of NPIs in 2020 may have had significant inhibitory effects on the incidence of respiratory infectious diseases, intestinal infectious diseases and sexually transmitted or bloodborne diseases. The relative reduction in NIDs during different emergency response levels in 2020 showed a declining trend as the response level changed from level 1 to level 3. These results can serve as essential guidance for policy-makers and stakeholders to take specific actions to control infectious diseases and protect vulnerable populations in the future.

Effect of voluntary human mobility restrictions on vector-borne diseases during the COVID-19 pandemic in Japan: A descriptive epidemiological study using a national database (2016 to 2021).

The coronavirus disease 2019 (COVID-19) pandemic not only encouraged people to practice good hygiene but also caused behavioral inhibitions and resulted reduction in both endemic and imported infectious diseases. However, the changing patterns of vector-borne diseases under human mobility restrictions remain unclear. Hence, we aimed to investigate the impact of transborder and local mobility restrictions on vector-borne diseases through a descriptive epidemiological study. The analysis was conducted using data from the National Epidemiological Surveillance of Infectious Diseases system in Japan. We defined the pre-pandemic period as the period between the 1st week of 2016 to the 52nd week of 2019 and defined the pandemic period as from the 1st week of 2020 to the 52nd week of 2021, with the assumption that human mobility was limited throughout the pandemic period. This study addressed 24 diseases among notifiable vector borne diseases. Datasets were obtained from weekly reports from the National Epidemiological Surveillance of Infectious Diseases, and the incidence of each vector-borne disease was examined. Interrupted time series analysis was conducted on the epidemic curves for the two periods. Between the pre- and post-pandemic periods, the incidence of dengue fever and malaria significantly decreased, which may be related to limited human transboundary mobility (p = 0.003/0.002). The incidence of severe fever with thrombocytopenia syndrome, scrub typhus, and Japanese spotted fever did not show changes between the two periods or no association with human mobility. This study suggests that behavioral control may reduce the incidence of new mosquito-borne diseases from endemic areas but may not affect tick-borne disease epidemics within an endemic area.

Impact of pandemics and disruptions to vaccination on infectious diseases epidemiology past and present.

Infectious diseases are a leading cause of morbidity and mortality worldwide with vaccines playing a critical role in preventing deaths. To better understand the impact of low vaccination rates and previous epidemics on infectious disease rates, and how these may help to understand the potential impacts of the current coronavirus disease 2019 (COVID-19) pandemic, a targeted literature review was conducted. Globally, studies suggest past suboptimal vaccine coverage has contributed to infectious disease outbreaks in vulnerable populations. Disruptions caused by the COVID-19 pandemic have contributed to a decline in vaccination uptake and a reduced incidence in several infectious diseases; however, these rates have increased following the lifting of COVID-19 restrictions with modeling studies suggesting a risk of increased morbidity and mortality from several vaccine-preventable diseases. This suggests a window of opportunity to review vaccination and infectious disease control measures before we see further disease resurgence in populations and age-groups currently unaffected.

Invited Commentary: To Make Long-Term Gains Against Infection Inequity, Infectious Disease Epidemiology Needs to Develop a More Sociological Imagination.

In a recent article in the Journal, Noppert et al. (Am J Epidemiol. 2023;192(3):475-482) articulated in detail the mechanisms connecting high-level "fundamental social causes" of health inequity to inequitable infectious disease outcomes, including infection, severe disease, and death. In this commentary, we argue that while intensive focus on intervening mechanisms is welcome and necessary, it cannot occur in isolation from examination of the way that fundamental social causes-including racism, socioeconomic inequity, and social stigma-sustain infection inequities even when intervening mechanisms are addressed. We build on the taxonomy of intervening mechanisms laid out by Noppert et al. to create a road map for strengthening the connection between fundamental cause theory and infectious disease epidemiology and discuss its implications for future research and intervention.

Heat waves accelerate the spread of infectious diseases.

COVID-19 pandemic appeared summer surge in 2022 worldwide and this contradicts its seasonal fluctuations. Even as high temperature and intense ultraviolet radiation can inhibit viral activity, the number of new cases worldwide has increased to >78% in only 1 month since the summer of 2022 under unchanged virus mutation influence and control policies. Using the attribution analysis based on the theoretical infectious diseases model simulation, we found the mechanism of the severe COVID-19 outbreak in the summer of 2022 and identified the amplification effect of heat wave events on its magnitude. The results suggest that approximately 69.3% of COVID-19 cases this summer could have been avoided if there is no heat waves. The collision between the pandemic and the heatwave is not an accident. Climate change is leading to more frequent extreme climate events and an increasing number of infectious diseases, posing an urgent threat to human health and life. Therefore, public health authorities must quickly develop coordinated management plans to deal with the simultaneous occurrence of extreme climate events and infectious diseases.

COVID-19 impact on EuroTravNet infectious diseases sentinel surveillance in Europe.

BACKGROUND: The COVID-19 pandemic resulted in a sharp decline of post-travel patient encounters at the European sentinel surveillance network (EuroTravNet) of travellers' health. We report on the impact of COVID-19 on travel-related infectious diseases as recorded by EuroTravNet clinics. METHODS: Travelers who presented between January 1, 2019 and September 30, 2021 were included. Comparisons were made between the pre-pandemic period (14 months from January 1, 2019 to February 29, 2020); and the pandemic period (19 months from March 1, 2020 to September 30, 2021). RESULTS: Of the 15,124 visits to the network during the 33-month observation period, 10,941 (72%) were during the pre-pandemic period, and 4183 (28%) during the pandemic period. Average monthly visits declined from 782/month (pre-COVID-19 era) to 220/month (COVID-19 pandemic era). Among non-migrants, the top-10 countries of exposure changed after onset of the COVID-19 pandemic; destinations such as Italy and Austria, where COVID-19 exposure peaked in the first months, replaced typical travel destinations in Asia (Thailand, Indonesia, India). There was a small decline in migrant patients reported, with little change in the top countries of exposure (Bolivia, Mali). The three top diagnoses with the largest overall decreases in relative frequency were acute gastroenteritis (-5.3%), rabies post-exposure prophylaxis (-2.8%), and dengue (-2.6%). Apart from COVID-19 (which rose from 0.1% to 12.7%), the three top diagnoses with the largest overall relative frequency increase were schistosomiasis (+4.9%), strongyloidiasis (+2.7%), and latent tuberculosis (+2.4%). CONCLUSIONS: A marked COVID-19 pandemic-induced decline in global travel activities is reflected in reduced travel-related infectious diseases sentinel surveillance reporting.

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.

[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.

Causality Network of Infectious Disease Revealed With Causal Decomposition.

Causal inference in the field of infectious disease attempts to gain insight into the potential causal nature of an association between risk factors and diseases. Simulated causality inference experiments have shown preliminary promise in improving understanding of the transmission of infectious diseases but still lack sufficient quantitative causal inference studies based on real-world data. Here, we investigate the causal interactions between three different infectious diseases and related factors, using causal decomposition analysis, to characterize the nature of infectious disease transmission. We show that the complex interactions between infectious disease and human behavior have a quantifiable impact on transmission efficiency of infectious diseases. Our findings, by shedding light on the underlying transmission mechanism of infectious diseases, suggest that causal inference analysis is a promising approach to determine epidemiological interventions.