Measuring preparedness to infectious diseases among people exposed to climate disasters in Cabo Delgado, Mozambique: a cross-sectional study.

INTRODUCTION: Climate change is contributing to increase the frequency and severity of climate disasters in Mozambique, leading, since 2019, to extensive damage to infrastructure and displacement 1.3 million people. Aim of this study is to evaluate baseline preparedness to vector-borne and water-borne infections among households and internally displaced people exposed to climate disasters in Mozambique. METHODS: This was a cross-sectional, community-based survey assessing the preparedness to infectious diseases outbreaks among people exposed to climate disasters in six districts in Mozambique. Structured form was delivered via face-to-face between October 15th and November 7th, 2022. Study outcome was defined as a seven-point score of preparedness to infectious disease outbreaks. Multivariable analysis of the score was conducted using Conway-Maxwell-Poisson regression. RESULTS: This study included 2,140 households and 11,239 people, with IDPs accounting for 30% of them. Overall, 1,186 (55.4%) households were overcrowded. Median score of preparedness was 3 points (IQR 2-4). At multivariable analyses, districts with low preparedness were Montepuez and Mueda. Higher preparedness was associated with family planning (p < 0.0001), access to primary education for all children living in the household (p < 0.001) and possession of a birth certificate for all children aged < 5y (p < 0.0001), while preparedness was heterogeneous among the districts (p < 0.05). Households composed by IDPs were not associated with a lower preparedness score. CONCLUSIONS: In climate-vulnerable communities in Mozambique, households practicing family planning, providing access to primary education and birth certificate for all children were less vulnerable to water-borne and vector-borne infectious disease outbreaks. Being family planning and childcare primarily performed by women, our findings can inform policymakers and practitioners on the importance of addressing women to mitigate the impact of climate disasters and reduce the risk of infectious disease outbreaks.

Use of Open-Source Epidemic Intelligence for Infectious Disease Outbreaks, Ukraine, 2022.

Formal infectious disease surveillance in Ukraine has been disrupted by Russia's 2022 invasion, leading to challenges with tracking and containing epidemics. To analyze the effects of the war on infectious disease epidemiology, we used open-source data from EPIWATCH, an artificial intelligence early-warning system. We analyzed patterns of infectious diseases and syndromes before (November 1, 2021-February 23, 2022) and during (February 24-July 31, 2022) the conflict. We compared case numbers for the most frequently reported diseases with numbers from formal sources and found increases in overall infectious disease reports and in case numbers of cholera, botulism, tuberculosis, HIV/AIDS, rabies, and salmonellosis during compared with before the invasion. During the conflict, although open-source intelligence captured case numbers for epidemics, such data (except for diphtheria) were unavailable/underestimated by formal surveillance. In the absence of formal surveillance during military conflicts, open-source data provide epidemic intelligence useful for infectious disease control.

Clinic of Hope: bridging infectious disease and orthopaedic surgery in prosthetic joint infection management.

Prosthetic joint infections (PJIs) following total joint arthroplasty are a significant and costly complication. To address fragmented care typically seen with separate management, we established a combined infectious disease and orthopaedic surgery clinic at Duke Health in July 2020. This clinic focuses on patients experiencing acute deterioration or multiple PJI episodes, often at the stage where amputation is the only option offered. From July 2021 to March 2024, the clinic completed 974 visits with 319 unique patients. The clinic maintained a low no-show rate of 5.0%. Treatment plans included procedures such as debridement, antibiotics and implant retention (38%), as well as implant explantation and one-stage exchange (32% each), with amputation required in only 4% of cases. The integrated clinic model facilitated real-time, multidisciplinary care, improving patient outcomes and operational efficiency. This approach offers a promising model for managing complex infections.

Possible counter-intuitive impact of local vaccine mandates for vaccine-preventable infectious diseases.

We modeled the impact of local vaccine mandates on the spread of vaccine-preventable infectious diseases, which in the absence of vaccines will mainly affect children. Examples of such diseases are measles, rubella, mumps, and pertussis. To model the spread of the pathogen, we used a stochastic SIR (susceptible, infectious, recovered) model with two levels of mixing in a closed population, often referred to as the household model. In this model, individuals make local contacts within a specific small subgroup of the population (e.g., within a household or a school class), while they also make global contacts with random people in the population at a much lower rate than the rate of local contacts. We considered what would happen if schools were given freedom to impose vaccine mandates on all of their pupils, except for the pupils that were exempt from vaccination because of medical reasons. We investigated first how such a mandate affected the probability of an outbreak of a disease. Furthermore, we focused on the probability that a pupil that was medically exempt from vaccination, would get infected during an outbreak. We showed that if the population vaccine coverage was close to the herd-immunity level, then both probabilities may increase if local vaccine mandates were implemented. This was caused by unvaccinated pupils possibly being moved to schools without mandates.

Focus, vigilance, resilience: towards stronger infectious disease surveillance, threat detection and response in the EU/EEA.

This perspective summarises and explains the long-term surveillance framework 2021-2027 for infectious diseases in the European Union/European Economic Area (EU/EEA) published in April 2023. It shows how shortcomings in the areas of public health focus, vigilance and resilience will be addressed through specific strategies in the coming years and how these strategies will lead to stronger surveillance systems for early detection and monitoring of public health threats as well as informing their effective prevention and control. A sharper public health focus is expected from a more targeted list of notifiable diseases, strictly public-health-objective-driven surveillance standards, and consequently, leaner surveillance systems. Vigilance should improve through mandatory event reporting, more automated epidemic intelligence processing and increased use of genomic surveillance. Finally, EU/EEA surveillance systems should become more resilient by modernising the underlying information technology infrastructure, expanding the influenza sentinel surveillance system to other respiratory viruses for better pandemic preparedness, and increasingly exploiting potentially more robust alternative data sources, such as electronic health records and wastewater surveillance. Continued close collaboration across EU/EEA countries will be key to ensuring the full implementation of this surveillance framework and more effective disease prevention and control.

NLRP3 Inflammasomes: Dual Function in Infectious Diseases.

The Nod-like receptor family pyrin domain containing 3 (NLRP3) inflammasome has been the most distinctive polymer protein complex. After recognizing the endogenous and exogenous danger signals, NLRP3 can cause inflammation by pyroptosis and secretion of mature, bioactive forms of IL-1ß and IL-18. The NLRP3 inflammasome is essential in the genesis and progression of infectious illnesses. Herein, we provide a comprehensive review of the NLRP3 inflammasome in infectious diseases, focusing on its two-sided effects. As an essential part of host defense with a protective impact, abnormal NLRP3 inflammasome activation, however, result in a systemic high inflammatory response, leading to subsequent damage. In addition, scientific evidence of small molecules, biologics, and phytochemicals acting on the NLRP3 inflammasome has been reviewed. We believe that the NLRP3 inflammasome helps us understand the pathological mechanism of different stages of infectious diseases and that inhibitors targeting the NLRP3 inflammasome will become a new and valuable research direction for the treatment of infectious diseases.

Outbreak detector: a web application to boost disease surveillance systems and timely detection of infectious disease epidemics.

OBJECTIVE: Digital technologies have improved the performance of surveillance systems through early detection of outbreaks and epidemic control. The aim of this study is to introduce an outbreak detection web application called OBDETECTOR (Outbreak Detector), which as a professional web application has the ability to process weekly or daily reported data from disease surveillance systems and facilitates the early detection of disease outbreaks. RESULTS: OBDETECTOR generates a histogram that exhibits the trend of infection within a time range selected by the user. The output comprises red triangles and plus signs, where the former denotes outbreak days determined by the algorithm applied to the data, and the latter represents days identified as outbreaks by the researcher. The graph also displays threshold values and its symbols enable researchers to compute evaluation criteria for outbreak detection algorithms, including sensitivity and specificity. OBDETECTOR allows users to modify algorithm parameters based on their research objectives immediately after loading data. The implementation of automatic web applications results in immediate reporting, precise analysis, and prompt alert notification. Moreover, Public Health authorities and other stakeholders of surveillance can benefit from the widespread accessibility and user-friendliness of these tools, enhancing their knowledge and skills for better engagement in surveillance programs.

Healthcare-seeking behavior and out-of-pocket payments in Erbil, Kurdistan Region of Iraq.

BACKGROUND: Understanding healthcare-seeking behavior and examining health expenditures can help determine possible barriers to accessing healthcare and direct more effective and inclusive healthcare systems. This study aimed to evaluate healthcare-seeking behavior and out-of-pocket healthcare expenditure in a sample of the population in Erbil, Iraq. METHODS: We conducted this cross-sectional study in Erbil, Kurdistan Region of Iraq, from October to December 2023. A convenience sample of 414 adults completed a self-administered online survey. The following data were collected: recent illness, sociodemographic characteristics, type of healthcare received, and cost of healthcare. RESULTS: The most common health conditions reported were communicable diseases (16.3%), musculoskeletal problems (13.1%), and noncommunicable diseases (12.7%). Approximately 85% of patients with health conditions requiring care sought healthcare; most visited private clinics (46.3%) and private hospitals (18.6%). The median total out-of-pocket healthcare expenditure in US dollars was 117.3 (interquartile range (IQR) = 45.6-410.0). The median total cost was much greater for participants who first visited a private health facility (USD 135.5, IQR = 57.3-405.6) than those who first visited a public facility (USD 76.8, IQR = 16.1-459.7). Participants ≥ 60 years spent significantly more than those < 14 years (USD 332, 95% CI = 211-453, p < 0.001). Evermarried participants spent significantly more than unmarried (USD 97, 95% CI = 1 to 192, p = 0.047). Health expenditures were significantly greater for noncommunicable diseases than infectious diseases (USD 232, 95% CI = 96-368, p = 0.001). After adjusting for covariates, age ≥ 60 years was independently associated with higher spending (USD 305, 95% CI = 153-457, p < 0.001). CONCLUSIONS: Most participants sought care from formal health services, preferring the private sector. Seeking care from private facilities incurred significantly higher costs than seeking care from public ones, which suggests potential barriers to accessing healthcare, particularly affordability. The findings underscore the importance of evaluating existing healthcare policies to enhance effectiveness and identify areas for improvement. This study can help policymakers and healthcare providers design effective interventions, allocate resources efficiently, and improve healthcare delivery.

Construction of effective reproduction number of infectious disease individuals based on spatiotemporal discriminant search model: take hand-foot-mouth disease as an example.

OBJECTIVE: In order to facilitate the tracing of infectious diseases in a small area and to effectively carry out disease control and epidemiological investigations, this research proposes a novel spatiotemporal model to estimate effective reproduction number(Re)for infectious diseases, based on the fundamental concept of contact tracing. METHODS: This study utilizes the incidence of hand, foot, and mouth disease (HFMD) among children in Bishan District, Chongqing, China from 2015 to 2019. The study incorporates the epidemiological characteristics of HFMD and aims to construct a Spatiotemporal Correlation Discrimination of HFMD. Utilizing ARC ENGINE and C# programming for the creation of a spatio-temporal database dedicated to HFMD to facilitate data collection and analysis. The scientific validity of the proposed method was verified by comparing the effective reproduction number obtained by the traditional SEIR model. RESULTS: We have ascertained the optimal search radius for the spatiotemporal search model to be 1.5 km. Upon analyzing the resulting Re values, which range from 1.14 to 4.75, we observe a skewed distribution pattern from 2015 to 2019. The median and quartile Re value recorded is 2.42 (1.98, 2.72). Except for 2018, the similarity coefficient r of the years 2015, 2016, 2017, and 2019 were all close to 1, and p <0.05 in the comparison of the two models, indicating that the Re values obtained by using the search model and the traditional SEIR model are correlated and closely related. The results exhibited similarity between the Re curves of both models and the epidemiological characteristics of HFMD. Finally, we illustrated the regional distribution of Re values obtained by the search model at various time intervals on Geographic Information System (GIS) maps which highlighted variations in the incidence of diseases across different communities, neighborhoods, and even smaller areas. CONCLUSION: The model comprehensively considers both temporal variation and spatial heterogeneity in disease transmission and accounts for each individual's distinct time of onset and spatial location. This proposed method differs significantly from existing mathematical models used for estimating Re in that it is founded on reasonable scientific assumptions and computer algorithms programming that take into account real-world spatiotemporal factors. It is particularly well-suited for estimating the Re of infectious diseases in relatively stable mobile populations within small geographical areas.

rtestim: Time-varying reproduction number estimation with trend filtering.

To understand the transmissibility and spread of infectious diseases, epidemiologists turn to estimates of the instantaneous reproduction number. While many estimation approaches exist, their utility may be limited. Challenges of surveillance data collection, model assumptions that are unverifiable with data alone, and computationally inefficient frameworks are critical limitations for many existing approaches. We propose a discrete spline-based approach that solves a convex optimization problem-Poisson trend filtering-using the proximal Newton method. It produces a locally adaptive estimator for instantaneous reproduction number estimation with heterogeneous smoothness. Our methodology remains accurate even under some process misspecifications and is computationally efficient, even for large-scale data. The implementation is easily accessible in a lightweight R package rtestim.

A novel method to select time-varying multivariate time series models for the surveillance of infectious diseases.

BACKGROUND: Describing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection. METHODS: This study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China. RESULTS: When the spatio-temporal variation was small (time delay coefficient: 0.1-0.2, spatial sparsity:0.1-0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2-0.3, spatial sparsity: 0.6-0.9). CONCLUSION: This study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators-the time delay coefficient and spatial sparsity-into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications.

Differences between males and females in infectious diseases notifications in the EU/EEA, 2012 to 2021.

BackgroundThere are differences between males and females for most diseases both for exposure and course of illness, including outcome. These differences can be related to biological sex or gender i.e. socio-cultural factors that may impact exposure and healthcare access.AimWe aimed to quantify differences between males and females in infectious disease notifications in Europe and identify countries with these differences significantly different from the European Union and European Economic Area (EU/EEA) average.MethodsNotifiable infectious disease surveillance data are reported by EU/EEA countries to ECDC. We retrieved surveillance data for 2012-2021. Using a cut-off median of annual disability-adjusted life years above 1 per 100,000 population, we included 16 infectious diseases. We calculated median male proportion and interquartile range by disease, year, country and age group and used boxplots to identify outliers.ResultsFor campylobacteriosis, acute hepatitis B, Legionnaires' disease, malaria and HIV and AIDS, all countries had male proportion above 50%. Most countries had a male proportion below 50% for pertussis (25/28 countries), STEC infection (21/28 countries) and Chlamydia trachomatis infection (16/24 countries). Chlamydia trachomatis infection and listeriosis showed the greatest dispersion of male proportion across age groups. Most outliers were countries reporting few cases.ConclusionWe observed important differences in male proportion across infectious disease notifications in EU/EEA countries. For some diseases with high male proportions in all countries, such as HIV and hepatitis B, behaviours play a role in disease transmission. Screening offered to specific populations may explain differences across countries for example for C. trachomatis infection.

Interacting particle models on the impact of spatially heterogeneous human behavioral factors on dynamics of infectious diseases.

Human behaviors have non-negligible impacts on spread of contagious disease. For instance, large-scale gathering and high mobility of population could lead to accelerated disease transmission, while public behavioral changes in response to pandemics may effectively reduce contacts and suppress the peak of the outbreak. In order to understand how spatial characteristics like population mobility and clustering interplay with epidemic outbreaks, we formulate a stochastic-statistical environment-epidemic dynamic system (SEEDS) via an agent-based biased random walk model on a two-dimensional lattice. The "popularity" and "awareness" variables are taken into consideration to capture human natural and preventive behavioral factors, which are assumed to guide and bias agent movement in a combined way. It is found that the presence of the spatial heterogeneity, like social influence locality and spatial clustering induced by self-aggregation, potentially suppresses the contacts between agents and consequently flats the epidemic curve. Surprisedly, disease responses might not necessarily reduce the susceptibility of informed individuals and even aggravate disease outbreak if each individual responds independently upon their awareness. The disease control is achieved effectively only if there are coordinated public-health interventions and public compliance to these measures. Therefore, our model may be useful for quantitative evaluations of a variety of public-health policies.

Assessing vaccine efficacy for infectious diseases with variable immunity using a mathematical model.

This study introduces an SIRS compartmental mathematical model encompassing vaccination and variable immunity periods for infectious diseases. I derive a basic reproduction number formula and assess the local and global stability of disease-free and the local stability of the endemic equilibria. I demonstrate that the basic reproduction number in the presence of a vaccine is highly sensitive to the rate of immunity loss, and even a slight reduction in this rate can significantly contribute to disease control. Additionally, I have derived a formula to calculate the critical efficacy period required for a vaccine to effectively manage and control the disease.The analysis conducted for the model suggests that increasing the vaccine's immunity duration (efficacy) decelerates disease dynamics, leading to reduced rates of reinfection and less severe disease outcomes. Furthermore, this delay contributes to a decrease in the basic reproduction number ( R 0 ), thus facilitating more rapid disease control efforts.

Language discrepancies in the performance of generative artificial intelligence models: an examination of infectious disease queries in English and Arabic.

BACKGROUND: Assessment of artificial intelligence (AI)-based models across languages is crucial to ensure equitable access and accuracy of information in multilingual contexts. This study aimed to compare AI model efficiency in English and Arabic for infectious disease queries. METHODS: The study employed the METRICS checklist for the design and reporting of AI-based studies in healthcare. The AI models tested included ChatGPT-3.5, ChatGPT-4, Bing, and Bard. The queries comprised 15 questions on HIV/AIDS, tuberculosis, malaria, COVID-19, and influenza. The AI-generated content was assessed by two bilingual experts using the validated CLEAR tool. RESULTS: In comparing AI models' performance in English and Arabic for infectious disease queries, variability was noted. English queries showed consistently superior performance, with Bard leading, followed by Bing, ChatGPT-4, and ChatGPT-3.5 (P = .012). The same trend was observed in Arabic, albeit without statistical significance (P = .082). Stratified analysis revealed higher scores for English in most CLEAR components, notably in completeness, accuracy, appropriateness, and relevance, especially with ChatGPT-3.5 and Bard. Across the five infectious disease topics, English outperformed Arabic, except for flu queries in Bing and Bard. The four AI models' performance in English was rated as "excellent", significantly outperforming their "above-average" Arabic counterparts (P = .002). CONCLUSIONS: Disparity in AI model performance was noticed between English and Arabic in response to infectious disease queries. This language variation can negatively impact the quality of health content delivered by AI models among native speakers of Arabic. This issue is recommended to be addressed by AI developers, with the ultimate goal of enhancing health outcomes.

Impact of twelve immunization-preventable infectious diseases on population health using disability-adjusted life years (DALYs) in Spain.

BACKGROUND: The objective of this study is to estimate the burden of selected immunization-preventable infectious diseases in Spain using the Burden of Communicable Diseases in Europe (BCoDE) methodology, as well as focusing on the national immunization programme and potential new inclusions. METHODS: The BCoDE methodology relies on an incidence and pathogen-based approach to calculate disease burden via disability-adjusted life year (DALY) estimates. It considers short and long-term sequelae associated to an infection via outcome trees. The BCoDE toolkit was used to populate those trees with Spanish-specific incidence estimates, and de novo outcome trees were developed for four infections (herpes zoster, rotavirus, respiratory syncytial virus [RSV], and varicella) not covered by the toolkit. Age/sex specific incidences were estimated based on data from the Spanish Network of Epidemiological Surveillance; hospitalisation and mortality rates were collected from the Minimum Basic Data Set. A literature review was performed to design the de novo models and obtain the rest of the parameters. The methodology, assumptions, data inputs and results were validated by a group of experts in epidemiology and disease modelling, immunization and public health policy. RESULTS: The total burden of disease amounted to 163.54 annual DALYs/100,000 population. Among the selected twelve diseases, respiratory infections represented around 90% of the total burden. Influenza exhibited the highest burden, with 110.00 DALYs/100,000 population, followed by invasive pneumococcal disease and RSV, with 25.20 and 10.57 DALYs/100,000 population, respectively. Herpes zoster, invasive meningococcal disease, invasive Haemophilus influenza infection and hepatitis B virus infection ranked lower with fewer than 10 DALYs/100,000 population each, while the rest of the infections had a limited burden (< 1 DALY/100,000 population). A higher burden of disease was observed in the elderly (≥ 60 years) and children < 5 years, with influenza being the main cause. In infants < 1 year, RSV represented the greatest burden. CONCLUSIONS: Aligned with the BCoDE study, the results of this analysis show a persisting high burden of immunization-preventable respiratory infections in Spain and, for the first time, highlight a high number of DALYs due to RSV. These estimates provide a basis to guide prevention strategies and make public health decisions to prioritise interventions and allocate healthcare resources in Spain.

The impact of population influx on infectious diseases - from the mediating effect of polluted air transmission.

The global population influx during the COVID-19 pandemic poses significant challenges to public health, making the prevention and control of infectious diseases a pressing concern. This paper aims to examine the impact of population influx on the spread of infectious diseases, with a specific emphasis on the mediating role of air pollution in this process. A theoretical analysis is conducted to explore the relationship between population influx, air pollution, and infectious diseases. Additionally, we establish a series of econometric models and employ various empirical tests and analytical techniques, including mediation effect test, threshold effect test, and systematic GMM test, to evaluate our hypotheses. The results indicate that: (1) Population influx directly and indirectly impacts infectious diseases. Specifically, population influx not only directly elevates the risk of infectious diseases, but also indirectly increases the incidence rate of infectious diseases by intensifying air pollution. (2) The impact of population inflow on infectious diseases exhibits regional heterogeneity. Compared to central and western China, the eastern regions exhibit a significantly higher risk of infectious diseases, exceeding the national average. (3) External factors influence the relationship between population influx and infectious diseases differently. Personal income and medical resources both help mitigate the risk of infectious diseases due to population influx, with medical resources having a more substantial effect. Contrary to expectations, abundant educational resources have not reduced the risk, instead, they have exacerbated the risk associated with population influx. This paper provides a scientific basis for formulating effective strategies for the prevention and control of infectious diseases.

Effect of individual activity level heterogeneity on disease spreading in higher-order networks.

The active state of individuals has a significant impact on disease spread dynamics. In addition, pairwise interactions and higher-order interactions coexist in complex systems, and the pairwise networks proved insufficient for capturing the essence of complex systems. Here, we propose a higher-order network model to study the effect of individual activity level heterogeneity on disease-spreading dynamics. Activity level heterogeneity radically alters the dynamics of disease spread in higher-order networks. First, the evolution equations for infected individuals are derived using the mean field method. Second, numerical simulations of artificial networks reveal that higher-order interactions give rise to a discontinuous phase transition zone where the coexistence of health and disease occurs. Furthermore, the system becomes more unstable as individual activity levels rise, leading to a higher likelihood of disease outbreaks. Finally, we simulate the proposed model on two real higher-order networks, and the results are consistent with the artificial networks and validate the inferences from theoretical analysis. Our results explain the underlying reasons why groups with higher activity levels are more likely to initiate social changes. Simultaneously, the reduction in group activity, characterized by measures such as "isolation," emerges as a potent strategy for disease control.