A novel approach to model the role of mobility suppression and vaccinations in containing epidemics in a network of cities

This paper presents a comprehensive agent-based model for the spread of an infection in a network of cities. Directional mobility is defined between each two cities and can take different values. The work examines the role that such mobility levels play in containing the infection with various vaccination coverage and age distributions. The results indicate that mobility reduction is sufficient to control the disease under all circumstances and full lockdowns are not a necessity. It has to be reduced to different ratios depending on the vaccination level and age distribution. A key finding is that increasing vaccination coverage above a certain level does not affect the mobility suppression level required to control the infection anymore for the cases of young population and heterogeneous age distributions. By investigating several migration and commuting patterns, it is found that shutting mobility in a few local places is favored against reducing mobility over the entire country network. In addition, commuting -and not migration-influences the spread level of the infection. The work offers an exclusive combined network-based and agent-based model that makes use of randomly generated mobility matrices.

What do clinicians mean by epidemics' preparedness

BACKGROUND

Infectious disease pandemics and epidemics pose significant global threats, and the risk of emerging infectious diseases has increased because of factors such as international connections, travel, and population density. Despite investments in global health surveillance, much of the world remains unprepared to manage infectious disease threats.

OBJECTIVES

This review article discusses the general considerations and lessons learned from the COVID-19 pandemic in terms of epidemic preparedness.

SOURCES

Non-systematic search on PubMed, scientific society websites, and scientific newspapers (performed in April 2023).

CONTENT

Key factors for preparedness include robust public health infrastructure, adequate allocation of resources, and effective communication between stakeholders. This narrative review emphasizes the need for timely and accurate dissemination of medical knowledge, as well as addressing the challenges of misinformation and infodemics. It also highlights the importance of quick availability of diagnostic tests and vaccines, ensuring equitable access to these technologies. The role of scientific coordination in developing treatment strategies and the safety and mental well-being of healthcare workers are discussed. Lastly, it should be emphasized the need for medical training, multidisciplinary teams, new technologies and artificial intelligence, and the active role of infectious disease physicians in epidemic preparedness efforts.

IMPLICATIONS

From clinicians' perspective, healthcare authorities play a crucial role in epidemic preparedness even by providing resource management plans, ensuring availability of essential supplies and training, facilitating communication, and improving safe infection management.

Vaccine-Preventable Disease Outbreaks among Healthcare Workers: A Scoping Review

BACKGROUND

Outbreaks of vaccine preventable diseases (VPDs) in health care workers (HCWs) can result in morbidity and mortality and cause significant disruptions to health care services, patients and visitors as well as an added burden on the health system. This scoping review is aimed to describe the epidemiology of VPD outbreaks in HCW, caused by diseases which are prevented by the ten vaccines recommended by World Health Organization (WHO) for HCWs.

METHODS

In April 2022 CINAHL, MEDLINE, Global Health and EMBASE were searched for all articles reporting on VPD outbreaks in HCWs since the year 2000. Articles were included regardless of language and study type. Clinical and epidemiological characteristics of VPD outbreaks were described.

RESULTS

Our search found 9363 articles, of which 216 met inclusion criteria. Studies describing six of the ten VPDs were found: influenza, measles, varicella, tuberculosis, pertussis and rubella. Most articles (93%) were from high- and upper middle-income countries. While most outbreaks occurred in hospitals, several influenza outbreaks were reported in long term care facilities. Based on available data, vaccination rates amongst HCWs were rarely reported.

CONCLUSION

We describe several VPD outbreaks in HCWs from 2000 to April 2022. The review emphasises the need to understand the factors influencing outbreaks in HCWs and highlight importance of vaccination amongst HCWs.

[Medical practice in classical Greece : The dermatological case reports in the Hippocratic Corpus]

OBJECTIVES

Which theoretical and practical competences do the dermatological case histories of the Hippocratic Corpus convey?

MATERIALS

The 431 Hippocratic case histories have been studied for reports and communication on diagnostics, therapy and prognosis of individuals and groups of patients suffering from skin diseases.

RESULTS

Within the seven books of the Hippocratic 'Epidemics', a total of 49 patients with dermatological symptoms are described; in 20 of these, skin disease was the leading suffering. The essential clinical signs were itching, red spots, blisters, pustules, aphthae, lichen, dandruff and hair loss. Most patients were male; one of the four women was pregnant, among the two children was a baby whose skin disease ended fatally. Eight patients were named. In addition, five waves of disease are reported, in the course of which the shape of the skin played an important role. The diagnostic workup was limited to inspection and palpation, but included the determination of the level of suffering. The follow-up checks often extended over months. Mostly, plasters and compresses with grain flour were primarily used for treatment.

CONCLUSION

The Hippocratic doctor observed and described a large number of skin lesions and attempted to classify them in the differential diagnosis. In addition to changes in the color and tension of the skin, numerous lesions-for which the names are still often used in modern dermatology-are also described in detail. The skin manifestations associated with fever and abdominal symptoms are counted among the epidemic diseases. The condition of the skin was often regarded as an indicator of general health. In the case reports, conservative treatment adapted to the development of the disease dominated. In desperate cases, bloodletting was chosen as a last resort.

Mortality in Norway and Sweden during the COVID-19 pandemic 2020-22: A comparative study

BACKGROUND

Norway and Sweden picked two different ways to mitigate the dissemination of the SARS-CoV-2 virus. Norway introduced the strictest lockdown in Europe with strict border controls and intense virus tracking of all local outbreaks while Sweden did not. That resulted in 477 COVID-19 deaths (Norway) and 9737 (Sweden) in 2020, respectively.

METHODS

Weekly number of COVID-19 related deaths and total deaths for 2020-22 were collected as well as weekly number of deaths for 2015-19 which were used as controls when calculating excess mortality. During the first 12-18 months with high rate of virus transmission in the society, excess mortality rates were used as substitute for COVID-19 deaths. When excess mortality rates later turned negative because of mortality displacement, COVID-19 deaths adjusted for bias due to overreporting were used.

RESULTS

There were 17521 COVID-19 deaths in Sweden and 4272 in Norway in the study period. The rate ratio (RR) of COVID-19 related deaths in Sweden vs. Norway to the end of week 43, 2022, was 2.11 (95% CI 2.05-2.19). RR of COVID-19 related deaths vs. excess number of deaths were 2.5 (Sweden) and 1.3 (Norway), respectively. RR of COVID-19 deaths in Sweden vs. Norway after adjusting for mortality displacement and lockdown, was 1.35 (95% CI 1.31-1.39), corresponding to saving 2025 life in Norway. If including all deaths in 2022, RR= 1.28 (95% CI 1.24-1.31).

CONCLUSIONS

Both COVID-19 related mortality and excess mortality rates are biased estimates. When adjusting for bias, mortality differences declined over time to about 30% higher mortality in Sweden after 30 months with pandemics.

[The urological case reports of the Hippocratic Corpus : Medical practice in classical Greece]

OBJECTIVE

Which theoretical and practical competences do the urologic case histories of the Hippocratic Corpus convey?

MATERIALS AND METHODS

The 431 Hippocratic case histories have been studied for reports and communication on diagnostic methods, treatment, and prognosis related to urologic diseases.

RESULTS

Within the seven books of the Hippocratic Epidemics, a total of 69 patients with urologic symptoms are described; in 21 cases the urologic disease is dominant. The leading clinical signs were urine discoloration and urinary sediment, polyuria, testicular swelling and pain, hematuria, stranguria, anuria, and renal pain, the most frequent diseases were nephritis and urolithiasis. Most patients were men in juvenile and adult age; 33 sick persons are named. The statements of patients regarding the course of their illness were often sparse. Diagnostic tests were restricted to inspection and palpation. Otherwise, treatment was confined to conservative measures. The dietary decisions for treatment of nephritis were differentiated but nonuniform.

CONCLUSION

The Hippocratic physician diagnosed and treated diseases of the urinary tract. Inflammatory conditions and traumatic lesions are described in detail. Highest priority was awarded to uroscopy. Nephritis was a widespread disorder und usually associated with other symptoms, above all fever. The urinary tract was often regarded as an indicator of physical and mental health. Conservative treatment measures dominate in the case reports.

Adolescent Mental Health and Resilience Before and During the COVID-19 Pandemic

PURPOSE

We aimed to assess levels of depression, anxiety, and resilience factors before and during the COVID-19 pandemic in a school sample of adolescents. We also aimed to examine the compensatory and protective effects of individual, family, and school resilience factors on adolescent mental health.

METHODS

We used fall 2019 and fall 2020 survey responses from a cluster randomized controlled trial implemented in 20 schools in a Midwestern county. The sample consisted of 3,085 responses from students in grades 5 and 6. Multilevel mixed-effects models with cluster robust standard errors were used to investigate the associations between exposure to the COVID-19 pandemic, mental health (anxiety, depression), and resilience factors (future orientation, family engagement, and having a caring school adult).

RESULTS

Anxiety, but not depression, was higher in fall 2020 compared to fall 2019. Family engagement increased during the pandemic, while future orientation of the student body was lower during that time and the prevalence of having a caring adult at school was unchanged. A positive future orientation was associated with lower levels of anxiety and depression, while having a caring school adult was associated with lower depression. Adolescents with less positive future orientations, low family engagement, and no caring school adults experienced the greatest increases in anxiety.

DISCUSSION

Positive future orientations, family engagement, and supportive nonparental adult relationships had compensatory and protective effects on adolescent mental health during the COVID-19 pandemic. Adding these measures to the inventory of modifiable resilience factors during natural disasters may promote healthy adaptation among adolescents.

Early warning of potential epidemics: A pilot application of an early warning tool to data from the pulmonary clinic of the university hospital of Thessaly, Greece

BACKGROUND & METHODS

This paper describes a pilot application of the Epidemic Volatility Index (EVI) to data from the pulmonary clinic of the University Hospital of Thessaly, Greece, for monitoring respiratory infections, COVID-19, and flu cases. EVI, a simple and easily implemented early warning method based on the volatility of newly reported cases, exhibited consistent and stable performance in detecting new waves of epidemics. The study highlights the importance of implementing early warning tools to address the effects of epidemics, including containment of outbreaks, timely intervention strategies, and resource allocation within real-world clinical settings as part of a broader public health strategy.

RESULTS

The results presented in the figures demonstrate the association between successive early warnings and the onset of new waves, providing valuable insights for proactive decision-making. A web-based application enabling real-time monitoring and informed decision-making by healthcare professionals, public health officials, and policymakers was developed.

CONCLUSIONS

This study emphasizes the significant role of early warning methods in managing epidemics and safeguarding public health. Future research may explore extensions and combinations of multiple warning systems for optimal outbreak interventions and application of the methods in the context of personalized medicine.

Differences between the true reproduction number and the apparent reproduction number of an epidemic time series

The time-varying reproduction number R(t) measures the number of new infections per infectious individual and is closely correlated with the time series of infection incidence by definition. The timings of actual infections are rarely known, and analysis of epidemics usually relies on time series data for other outcomes such as symptom onset. A common implicit assumption, when estimating R(t) from an epidemic time series, is that R(t) has the same relationship with these downstream outcomes as it does with the time series of incidence. However, this assumption is unlikely to be valid given that most epidemic time series are not perfect proxies of incidence. Rather they represent convolutions of incidence with uncertain delay distributions. Here we define the apparent time-varying reproduction number, RA(t), the reproduction number calculated from a downstream epidemic time series and demonstrate how differences between RA(t) and R(t) depend on the convolution function. The mean of the convolution function sets a time offset between the two signals, whilst the variance of the convolution function introduces a relative distortion between them. We present the convolution functions of epidemic time series that were available during the SARS-CoV-2 pandemic. Infection prevalence, measured by random sampling studies, presents fewer biases than other epidemic time series. Here we show that additionally the mean and variance of its convolution function were similar to that obtained from traditional surveillance based on mass-testing and could be reduced using more frequent testing, or by using stricter thresholds for positivity. Infection prevalence studies continue to be a versatile tool for tracking the temporal trends of R(t), and with additional refinements to their study protocol, will be of even greater utility during any future epidemics or pandemics.

Estimating the population effectiveness of interventions against COVID-19 in France: A modelling study

BACKGROUND

Non-pharmaceutical interventions (NPIs) and vaccines have been widely used to manage the COVID-19 pandemic. However, uncertainty persists regarding the effectiveness of these interventions due to data quality issues, methodological challenges, and differing contextual factors. Accurate estimation of their effects is crucial for future epidemic preparedness.

METHODS

To address this, we developed a population-based mechanistic model that includes the impact of NPIs and vaccines on SARS-CoV-2 transmission and hospitalization rates. Our statistical approach estimated all parameters in one step, accurately propagating uncertainty. We fitted the model to comprehensive epidemiological data in France from March 2020 to October 2021. With the same model, we simulated scenarios of vaccine rollout.

RESULTS

The first lockdown was the most effective, reducing transmission by 84 % (95 % confidence interval (CI) 83-85). Subsequent lockdowns had diminished effectiveness (reduction of 74 % (69-77) and 11 % (9-18), respectively). A 6 pm curfew was more effective than one at 8 pm (68 % (66-69) vs. 48 % (45-49) reduction), while school closures reduced transmission by 15 % (12-18). In a scenario without vaccines before November 2021, we predicted 159,000 or 168 % (95 % prediction interval (PI) 70-315) more deaths and 1,488,000 or 300 % (133-492) more hospitalizations. If a vaccine had been available after 100 days, over 71,000 deaths (16,507-204,249) and 384,000 (88,579-1,020,386) hospitalizations could have been averted.

CONCLUSION

Our results highlight the substantial impact of NPIs, including lockdowns and curfews, in controlling the COVID-19 pandemic. We also demonstrate the value of the 100 days objective of the Coalition for Epidemic Preparedness Innovations (CEPI) initiative for vaccine availability.

Medical education and the epidemics: How educational technology responded

The global disruption created by the Coronavirus Disease 2019 (COVID-19) pandemic in medical education and healthcare institutions is unparalleled. Consequently, it is essential to evaluate the usability of forms of educational technology and to identify their viability and suitability for medical education. The objective of the investigation was to present an assessment of the state of medical education during the COVID-19 epidemic and to identify the obstacles faced by educators while introducing online learning systems for medical students. Two cross-sectional surveys were conducted with 200 medical students and 75 staff members from Saudi Arabia's University of Health. A descriptive method was used to focus on the mechanisms of analysis, foresight, and comprehension of reality. The most significant findings were the obstacles posed by instructors' most urgent requirements for educational technology training and its applications in order to activate distance education in medical education. In addition to a detailed description of the academic and technological concerns and obstacles encountered by students and faculty of health colleges during the pandemic, this report includes a discussion of the pandemic itself. Several prospective recommendations for the use of online and blended learning in health colleges post-pandemic were also made. Additionally, the requirement to activate learning via virtual professional learning groups.

[Retrospective estimation of initial COVID-19 infections in the Santiago Metropolitan Region in Chile]

OBJECTIVE

Estimate daily infections of COVID-19 during the first year of the pandemic in the Santiago Metropolitan Region (SRM) in Chile and Chile that are more realistic than those officially registered.

METHOD

Retrospective estimate of daily infections from daily data on COVID-19 deaths, a seroprevalence study, and the REMEDID (Retrospective Methodology to Estimate Daily Infections from Deaths) algorithm.

RESULTS

In SRM, it is observed that: 1) the maximum peak of infections was more than double that registered in the official statistics; 2) such peak was reached on May 22 (95% CI: 20-24 May), 2022, that is, 24 days before the official date of the peak of infections; and 3) the first estimated contagion took place on January 28, 2020 (95% CI: January 21 to February 16), that is, 36 days before the official date. In Chile, the situation is similar. During the first wave SRM accounted for 70%-76% of those infected in Chile, while from August 2020 onwards it accounted for 36%-39%.

CONCLUSIONS

The official records of COVID-19 infections in SRM and Chile underestimated the real number of positives and showed a delay of about a month in the dynamics of infections. This is not an isolated situation, as it is known to have been the case in other countries as well. However, it is important to have reliable estimates for a correct modeling of the spread of the virus.

A machine learning-based universal outbreak risk prediction tool

In order to prevent and control the increasing number of serious epidemics, the ability to predict the risk caused by emerging outbreaks is essential. However, most current risk prediction tools, except EPIRISK, are limited by being designed for targeting only one specific disease and one country. Differences between countries and diseases (e.g., different economic conditions, different modes of transmission, etc.) pose challenges for building models with cross-country and cross-disease prediction capabilities. The limitation of universality affects domestic and international efforts to control and prevent pandemic outbreaks. To address this problem, we used outbreak data from 43 diseases in 206 countries to develop a universal risk prediction system that can be used across countries and diseases. This system used five machine learning models (including Neural Network XGBoost, Logistic Boost, Random Forest and Kernel SVM) to predict and vote together to make ensemble predictions. It can make predictions with around 80%-90 % accuracy from economic, cultural, social, and epidemiological factors. Three different datasets were designed to test the performance of ML models under different realistic situations. This prediction system has strong predictive ability, adaptability, and generality. It can give universal outbreak risk assessment that are not limited by border or disease type, facilitate rapid response to pandemic outbreaks, government decision-making and international cooperation.

Experiences and challenges with the new European Clinical Trials Regulation

BACKGROUND

The new European Medicines Agency (EMA) Clinical Trials Information System (CTIS), based on the Clinical Trials Regulation (CTR EU 536/2014), came into full effect on 31 January 2022 and was intended to provide an easier, more streamlined approach to the registration of clinical trials taking place in Europe. Using the experience gained on the new regulatory framework from three multi-national European clinical research studies of outbreak-prone infectious diseases, this article describes the advantages and shortcomings of the new clinical trial submission procedure.

METHODS

We report the time to approval, size of the application dossier, and number of requests for information (RFIs) for each study. We also explore the experience of each study within the regulatory framework and its use of CTIS to document the real-world, practical consequences of the system on individual studies. The study assesses the experience of three multi-country studies conducted in Europe working within the EU and non-EU regulatory environments.

RESULTS

While the time to regulatory and ethical approval has improved since the implementation of the new regulation, the timelines for approvals are still unacceptably slow, particularly for studies being conducted in the context of an evolving outbreak. Within the new regulatory approval procedure, there is evidence of conflicting application requirements, increased document burden, barriers to submitting important modifications, and debilitating technical hurdles.

CONCLUSIONS

CTIS promised to lower the administrative bar, but unfortunately this has not been achieved. There are challenges that need to be urgently confronted and addressed for international research collaborators to effectively manage health crises in the future. While the value of multi-national outbreak research is clear, the limitations and delays imposed by the system, which raise challenging ethical questions about the regulation, are prejudicial to all clinical research, especially publicly funded academic studies. This report is relevant to both regulators and clinical researchers. It is hoped that these findings can help improve pan-European clinical trials, especially for the purpose of epidemic preparedness and response.

TRIAL REGISTRATION

This paper references experiences gained during management of three pan-European trials: EU-SolidAct's Bari-SolidAct (CT No. 2022-500385-99-00 - 15 March 2022) and AXL-SolidAct (CT No. 2022-500363-12-00 - 19 April 2022), and MOSAIC (CT No. 2022-501132-42-00 - 22 June 2022).

West Nile virus in Mexico: Why vectors matter for explaining the current absence of epidemics

Since 2002, West Nile Virus (WNV) has been reported in 18 states in Mexico, either by PCR or serological testing. However, it is believed that the virus is present in more states. Only four states (out of 32) have reported confirmed human cases, and one state has serological evidence. In the country, WNV is present in mainly horses and birds, but its presence extends to crocodiles, felines, canines, swines, donkeys, caprines, antilopes, cattle, bats, and camelids. Positive mosquito species include Aedes and Culex spp. Different hypotheses have been proposed to explain the absence of WNV epidemics in Latin America. Since some regions of Mexico and the United States share ecological and climatic conditions, these hypotheses may not be sufficient to account for the absence of WNV outbreaks or epidemics. This paper discusses the proposed ideas and attempts to contextualize them for Mexico, particularly for the U.S.-Mexico border, where WNV infections have been reported in humans, horses, and mosquitoes. We propose that integration of urban ecology and entomology knowledge is needed to better understand the absence of WN cases in Mexico.

A strategy for strengthening social science skills in Africa]

The Covid pandemic was a reminder of the need to be prepared for epidemics and pandemics and to take into account their socio-political dimensions by developing socioanthropological and interdisciplinary approaches. In the post-crisis era, the challenge is one of operationality. How can these dimensions be made more visible? How can we develop analyses that can help to humanize institutional responses, make inequalities visible to limit them during the crisis, reveal structural determinants of transmission, and define interventions that are scientifically sound, ethically just and respectful of diversity?Three strategies are relevant to meet these challenges: (1) more social scientists in Frenchspeaking Africa must get expertise on epidemics to investigate associated issues before, during and after epidemic crisis; (2) public health professionals, health and social workers must get informed about social, historical, economic and political aspects of epidemics that shape risk, care and control; (3) collaborations between researchers and those involved in responding to epidemics on the basis of shared knowledge must develop.This article presents a capacity-building initiative developed in French-speaking West Africa by the Anthropology of Emerging Epidemics Network (RAAE), in conjunction with other networks (Sonar-Global) and institutions (CRCF, IRD). It describes and analyzes a program that combines a working method, a scientific content and teaching tools. This program benefited from previous training experiences and gathered expertise from about 25 social scientists, mainly medical anthropologists, who have worked on various epidemics and pandemics such as AIDS, Ebola, plague, Covid and dengue in West Africa and beyond. The process to develop the course was based on workshops followed by redaction periods, then testings for content and tools during training sessions.The course focuses on two audiences: social science researchers (with a Master degree level and above) and social and health workers (public health, community health, NGOs, social workers). For the former, the course aims at reinforcing theoretical and methodological skills through the presentation of issues, key concepts, selected theoretical developments, themes and bibliographical references. For the latter, the course is based on modules about operational issues that can be taken separately, to better adapt the content of trainings to local teams' needs. For both, a glossary includes 100 definitions of public health, medical and social science vocabulary, relevant to epidemics. The content in terms of skills to be acquired (knowledge, know-how) is presented briefly in the article. Both the scientific content and learning methods and tools are presented in a manual (Desclaux et al., Anthropologie appliquée aux épidémies émergentes, 2022 [5]) as well as on Sonar-Global (English) and RAEE (French) websites (www.sonar-global.eu, www.raee.fr).The knowledge to be imparted is organized into 13 modules: introduction; the framework for responding to epidemics; emergence and One Health; antimicrobial resistance; infectious risk (inequalities, stigmatization and prevention); knowledge (circulation and interpretation); health services (places of risk and care); public health measures (lockdown and distancing); experiences (suffering of the sick and mobilization); death (meaning and rituals); vaccination (innovation, equity and hesitation); epidemic cycles (preparation, response and recovery); challenges, methods, ethics and governance; and conclusion.The first training courses held in Senegal and Burkina Faso for university researchers and young scholars from Africa and France were positively evaluated by the participants. They report that they have acquired knowledge in epidemic social science, but also in public health, which has given them the necessary basis for communicating and developing collaborations (in research and intervention) with social and health actors. The model could be duplicated with adaptation for new training sessions organized by other institutions, for which a manual is available.

Towards Inferring Network Properties from Epidemic Data

Epidemic propagation on networks represents an important departure from traditional mass-action models. However, the high-dimensionality of the exact models poses a challenge to both mathematical analysis and parameter inference. By using mean-field models, such as the pairwise model (PWM), the high-dimensionality becomes tractable. While such models have been used extensively for model analysis, there is limited work in the context of statistical inference. In this paper, we explore the extent to which the PWM with the susceptible-infected-recovered (SIR) epidemic can be used to infer disease- and network-related parameters. Data from an epidemics can be loosely categorised as being population level, e.g., daily new cases, or individual level, e.g., recovery times. To understand if and how network inference is influenced by the type of data, we employed the widely-used MLE approach for population-level data and dynamical survival analysis (DSA) for individual-level data. For scenarios in which there is no model mismatch, such as when data are generated via simulations, both methods perform well despite strong dependence between parameters. In contrast, for real-world data, such as foot-and-mouth, H1N1 and COVID19, whereas the DSA method appears fairly robust to potential model mismatch and produces parameter estimates that are epidemiologically plausible, our results with the MLE method revealed several issues pertaining to parameter unidentifiability and a lack of robustness to exact knowledge about key quantities such as population size and/or proportion of under reporting. Taken together, however, our findings suggest that network-based mean-field models can be used to formulate approximate likelihoods which, coupled with an efficient inference scheme, make it possible to not only learn about the parameters of the disease dynamics but also that of the underlying network.

Differential contagiousness of respiratory disease across the United States

The initial contagiousness of a communicable disease within a given population is quantified by the basic reproduction number, R0. This number depends on both pathogen and population properties. On the basis of compartmental models that reproduce Coronavirus Disease 2019 (COVID-19) surveillance data, we used Bayesian inference and the next-generation matrix approach to estimate region-specific R0 values for 280 of 384 metropolitan statistical areas (MSAs) in the United States (US), which account for 95% of the US population living in urban areas and 82% of the total population. We focused on MSA populations after finding that these populations were more uniformly impacted by COVID-19 than state populations. Our maximum a posteriori (MAP) estimates for R0 range from 1.9 to 7.7 and quantify the relative susceptibilities of regional populations to spread of respiratory diseases. ONE-SENTENCE SUMMARY: Initial contagiousness of Coronavirus Disease 2019 varied over a 4-fold range across urban areas of the United States.

Economic sanctions and spread of infectious diseases

Economic sanctions can induce economic crises and compromise the determinants of health. In the literature, economic crises have been found to increase the risk of infectious disease outbreaks. Presumably, sanctions can increase the risk of infectious disease spreads, indirectly. However, non-economic factors can fuel the adverse impact of sanctions, including political consequences of sanctions and civil war. We performed a systematic literature review of articles in Embase, MEDLINE, Scopus, Web of Science, Cochrane Library, and the grey literature to assess empirically the impact of economic sanctions on the spread of infectious diseases within and beyond the borders of sanctioned countries. Our review did not identify any study meeting our inclusion criteria. Most of the studies did not control for major socio-political events, particularly armed conflicts in the sanctioned countries. This discovery underscores a notable gap in the examination of the impact of economic sanctions on the propagation of infectious diseases, presenting a threat to global health. Using the social-ecological model, we hypothesize how the economic crisis resulting from economic sanctions affects determinants of health, increases the risk of the spread of infectious diseases and hinders the response capacity of health systems.

A deep-SIQRV epidemic model for COVID-19 to access the impact of prevention and control measures

The coronavirus (COVID-19) has mutated into several variants, and evidence says that new variants are more transmissible than existing variants. Even with full-scale vaccination efforts, the theoretical threshold for eradicating COVID-19 appears out of reach. This article proposes an artificial intelligence(AI) based intelligent prediction model called Deep-SIQRV(Susceptible-Infected-Quarantined-Recovered-Vaccinated) to simulate the spreading of COVID-19. While many models assume that vaccination provides lifetime protection, we focus on the impact of waning immunity caused by the conversion of vaccinated individuals back to susceptible ones. Unlike existing models, which assume that all coronavirus-infected individuals have the same infection rate, the proposed model considers the various infection rates to analyze transmission laws and trends. Next, we consider the influence of prevention and control strategies, such as media marketing and law enforcement, on the spread of the epidemic. We employed the PAN-LDA model to extract features from COVID-19-related discussions on social media and online news articles. Moreover, the Long Short Term Memory(LSTM) model and Evolution Strategies(ES) are used to optimize transmission rates of infection and other model parameters, respectively. The experimental results on epidemic data from various Indian states demonstrate that persons infected with coronavirus had a more significant infection rate within four to nine days after infection, which corresponds to the actual transmission laws of the epidemic. The experimental results show that the proposed model has good prediction ability and obtains the Mean Absolute Percentage Error(MAPE) of 0.875%, 0.965%, 0.298%, and 0.215% for the next eight days in Maharashtra, Kerala, Karnataka, and Delhi, respectively. Our findings highlight the significance of using vaccination data, COVID-19-related posts, and information generated by the government's tremendous efforts in the prediction calculation process.

Epidemic highs and lows: a stochastic diffusion model for active cases

We derive a stochastic epidemic model for the evolving density of infective individuals in a large population. Data shows main features of a typical epidemic consist of low periods interspersed with outbreaks of various intensities and duration. In our stochastic differential model, a novel reproductive term combines a factor expressing the recent notion of 'attenuated Allee effect' and a capacity factor is controlling the size of the process. Simulation of this model produces sample paths of the stochastic density of infectives, which behave much like long-time Covid-19 case data of recent years. Writing the process as a stochastic diffusion allows us to derive its stationary distribution, showing the relative time spent in low levels and in outbursts. Much of the behaviour of the density of infectives can be understood in terms of the interacting drift and diffusion coefficient processes, or, alternatively, in terms of the balance between noise level and the attenuation parameter of the Allee effect. Unexpected results involve the effect of increasing overall noise variance on the density of infectives, in particular on its level-crossing function.

The meteorological contrast index in the context of climate change and public health

In the context of climate change, extreme weather events and sudden shifts in weather patterns are becoming increasingly frequent. The atmosphere is considered a source of meteorological and climatic risks for human beings and living organisms. Numerous studies have examined the correlation between meteorological variables and human morbidity and mortality. However, only a few authors have investigated the impact of environmental changes on human health and, to our knowledge, there are no meteorological indices proposing a methodology for assessing changes in atmospheric conditions. Under the hypothesis that meteorological disruptions have an impact on human health, this article proposes a method to calculate a new index, the Meteorological Contrast Index (MCI), based on weather changes. This index takes into account three variables: i) categorization based on the type of atmospheric process, ii) changes in these categories over a specific time period, and iii) the level of stress associated with these changes, considering the severity of the transition from one category to another. If the predictive value of this index is proven for a specific meteorological variable and disease, it could be valuable in defining biometeorological early warning systems for the prevention and management of healthcare resources.•The Meteorological Contrast Index is the first index that proposes a method to assess changes in atmospheric conditions.•Atmospheric changes are a significant source of biometeorological distress, which can be quantitatively defined using the Meteorological Contrast Index.•Certain diseases are sensitive to the weather, and their incidence may increase under specific sequences of weather types.

Infectious diseases and the impact on transfusion medicine: A historical review and lessons for the future

Blood transfusion saves millions of lives each year. It is a well-established treatment, and many procedures are applied to avoid transmitted infections. However, throughout the history of transfusion medicine, many infectious diseases arose or were recognised, bringing up an impact on the blood supply, as the difficulties in diagnosing new diseases, the decrease in blood donors, the challenges for the medical team, the risks for the receptor and the related costs. This study aims to review historically the principal infectious diseases transmitted through the blood that circulated worldwide in the 20th and 21st centuries, considering the impact on the blood banks. Despite the current blood bank control of transfusion risks and the hemovigilance improvements, transmitted and emerging infections can still compromise the blood bank supply, as we have witnessed during the first waves of the COVID-19 pandemic. Moreover, new pathogens will continue emerging, and we must be prepared for the future.

Old and new aspects of influenza

Influenza is a classic infectious disease that, through the continuous variation of the viruses that produce it, imposes new challenges that we must solve as quickly as possible. The COVID-19 pandemic has substantially modified the behavior of influenza and other respiratory viruses, and in the coming years we will have to coexist with a new pathogen that will probably interact with existing pathogens in a way that we cannot yet glimpse. However, knowledge prior to the pandemic allows us to focus on the aspects that must be modified to make influenza an acceptable challenge for the future. In this review, emphasis is placed on the most relevant aspects of epidemiology, disease burden, diagnosis, and vaccine prevention, and how scientific and clinical trends in these aspects flow from the previously known to future challenges.

Effectiveness of testing, contact tracing and isolation interventions among the general population on reducing transmission of SARS-CoV-2: a systematic review

We conducted a systematic literature review of general population testing, contact tracing, case isolation and contact quarantine interventions to assess their effectiveness in reducing SARS-CoV-2 transmission, as implemented in real-world settings. We designed a broad search strategy and aimed to identify peer-reviewed studies of any design provided there was a quantitative measure of effectiveness on a transmission outcome. Studies that assessed the effect of testing or diagnosis on disease outcomes via treatment, but did not assess a transmission outcome, were not included. We focused on interventions implemented among the general population rather than in specific settings; these were from anywhere in the world and published any time after 1 January 2020 until the end of 2022. From 26 720 titles and abstracts, 1181 were reviewed as full text, and 25 met our inclusion criteria. These 25 studies included one randomized control trial (RCT) and the remaining 24 analysed empirical data and made some attempt to control for confounding. Studies included were categorized by the type of intervention: contact tracing (seven studies); specific testing strategies (12 studies); strategies for isolating cases/contacts (four studies); and 'test, trace, isolate' (TTI) as a part of a package of interventions (two studies). None of the 25 studies were rated at low risk of bias and many were rated as serious risk of bias, particularly due to the likely presence of uncontrolled confounding factors, which was a major challenge in assessing the independent effects of TTI in observational studies. These confounding factors are to be expected from observational studies during an on-going pandemic, when the emphasis was on reducing the epidemic burden rather than trial design. Findings from these 25 studies suggested an important public health role for testing followed by isolation, especially where mass and serial testing was used to reduce transmission. Some of the most compelling analyses came from examining fine-grained within-country data on contact tracing; while broader studies which compared behaviour between countries also often found TTI led to reduced transmission and mortality, this was not universal. There was limited evidence for the benefit of isolation of cases/contacts away from the home environment. One study, an RCT, showed that daily testing of contacts could be a viable strategy to replace lengthy quarantine of contacts. Based on the scarcity of robust empirical evidence, we were not able to draw any firm quantitative conclusions about the quantitative impact of TTI interventions in different epidemic contexts. While the majority of studies found that testing, tracing and isolation reduced transmission, evidence for the scale of this impact is only available for specific scenarios and hence is not necessarily generalizable. Our review therefore emphasizes the need to conduct robust experimental studies that help inform the likely quantitative impact of different TTI interventions on transmission and their optimal design. Work is needed to support such studies in the context of future emerging epidemics, along with assessments of the cost-effectiveness of TTI interventions, which was beyond the scope of this review but will be critical to decision-making. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.

[Early effect of vaccination on the positivity rate of salivary tests performed on nursing home staff in the Walloon Region]

In Belgium, nursing homes (NH) were disproportionately affected by the SARS-CoV-2 pandemic. The objective of this study was to compare the risk of SARS-CoV-2 infection in vaccinated and unvaccinated staff members.

METHODS

This was a prospective cohort study conducted between February 1 and April 02, 2021, in 99 nursing homes (NHs) in the Walloon Region, a few weeks after the start of the vaccination campaign. A mixed-effects logistic regression analysis was performed to assess the relationship between COVID results of molecular tests on saliva samples of the NHs' staff and their vaccination status.

RESULTS

Only 32 (0,1 %) of 39 267 saliva tests were positive. Logistic analysis showed that unvaccinated nursing home staff were 4 times more likely to develop COVID-19 than vaccinated staff during the study period.

CONCLUSION

This study demonstrated an early decreased risk of infection in vaccinated NHs staff. Saliva tests were designed to be convenient, less expensive and non-invasive, and could be considered as an alternative to nasopharyngeal tests.

Disentangling the role of virus infectiousness and awareness-based human behavior during the early phase of the COVID-19 pandemic in the European Union

In this work, we manage to disentangle the role of virus infectiousness and awareness-based human behavior in the COVID-19 pandemic. Using Bayesian inference, we quantify the uncertainty of a state-space model whose propagator is based on an unusual SEIR-type model since it incorporates the effective population fraction as a parameter. Within the Markov Chain Monte Carlo (MCMC) algorithm, Unscented Kalman Filter (UKF) may be used to evaluate the likelihood approximately. UKF is a suitable strategy in many cases, but it is not well-suited to deal with non-negativity restrictions on the state variables. To overcome this difficulty, we modify the UKF, conveniently truncating Gaussian distributions, which allows us to deal with such restrictions. We use official infection notification records to analyze the first 22 weeks of infection spread in each of the 27 countries of the European Union (EU). It is known that such records are the primary source of information to assess the early evolution of the pandemic and, at the same time, usually suffer underreporting and backlogs. Our model explicitly accounts for uncertainty in the dynamic model parameters, the dynamic model adequacy, and the infection observation process. We argue that this modeling paradigm allows us to disentangle the role of the contact rate, the effective population fraction, and the infection observation probability across time and space with an imperfect first principles model. Our findings agree with phylogenetic evidence showing little variability in the contact rate, or virus infectiousness, across EU countries during the early phase of the pandemic, highlighting the advantage of incorporating the effective population fraction into pandemic modeling for heterogeneity in both human behavior and reporting. Finally, to evaluate the consistency of our data assimilation method, we performed a forecast that adequately fits the actual data.

Statement of significance

Data-driven and model-based epidemiological studies aimed at learning the number of people infected early during a pandemic should explicitly consider the behavior-induced effective population effect. Indeed, the non-isolated, or effective, fraction of the population during the early phase of the pandemic is time-varying, and first-principles modeling with quantified uncertainty is imperative for an adequate analysis across time and space. We argue that, although good inference results may be obtained using the classical SEIR type model, the model posed in this work has allowed us to disentangle the role of virus infectiousness and awareness-based human behavior during the early phase of the COVID-19 pandemic in the European Union from official infection notification records.

EpiMix: A novel method to estimate effective reproduction number

Transmission potential of a pathogen, often quantified by the time-varying reproduction number Rt, provides the current pace of infection for a disease and indicates whether an emerging epidemic is under control. In this study, we proposed a novel method, EpiMix, for Rt estimation, wherein we incorporated the impacts of exogenous factors and random effects under a Bayesian regression framework. Using Integrated Nested Laplace Approximation, EpiMix is able to efficiently generate reliable, deterministic Rt estimates. In the simulations and case studies performed, we further demonstrated the method's robustness in low-incidence scenarios, together with other merits, including its flexibility in selecting variables and tolerance of varying reporting rates. All these make EpiMix a potentially useful tool for real-time Rt estimation provided that the serial interval distribution, time series of case counts and external influencing factors are available.

Density forecasting of conjunctivitis burden using high-dimensional environmental time series data

As one of the most common eye conditions being presented at clinics, acute conjunctivitis puts substantial strain on primary health resources. To reduce this public health burden, it is important to forecast and provide forward guidance to policymakers by estimating conjunctivitis trends, taking into account factors which influence transmission. Using a high-dimensional set of ambient air pollution and meteorological data, this study describes new approaches to point and probabilistic forecasting of conjunctivitis burden which can be readily translated to other infectious diseases. Over the period of 2012 - 2022, we show that simple models without environmental data provided better point forecasts but the more complex models which optimized predictive accuracy and combined multiple predictors demonstrated superior density forecast performance. These results were shown to be consistent over periods with and without structural breaks in transmission. Furthermore, ecological analysis using post-selection inference showed that increases in SO2, O3 surface concentration and total precipitation were associated to increased conjunctivitis attendance. The methods proposed can provide rich and informative forward guidance for outbreak preparedness and help guide healthcare resource planning in both stable periods of transmission and periods where structural breaks in data occur.

Source identification via contact tracing in the presence of asymptomatic patients

Inferring the source of a diffusion in a large network of agents is a difficult but feasible task, if a few agents act as sensors revealing the time at which they got hit by the diffusion. One of the main limitations of current source identification algorithms is that they assume full knowledge of the contact network, which is rarely the case, especially for epidemics, where the source is called patient zero. Inspired by recent implementations of contact tracing algorithms, we propose a new framework, which we call Source Identification via Contact Tracing Framework (SICTF). In the SICTF, the source identification task starts at the time of the first hospitalization, and initially we have no knowledge about the contact network other than the identity of the first hospitalized agent. We may then explore the network by contact queries, and obtain symptom onset times by test queries in an adaptive way, i.e., both contact and test queries can depend on the outcome of previous queries. We also assume that some of the agents may be asymptomatic, and therefore cannot reveal their symptom onset time. Our goal is to find patient zero with as few contact and test queries as possible. We implement two local search algorithms for the SICTF: the LS algorithm, which has recently been proposed by Waniek et al. in a similar framework, is more data-efficient, but can fail to find the true source if many asymptomatic agents are present, whereas the LS+ algorithm is more robust to asymptomatic agents. By simulations we show that both LS and LS+ outperform previously proposed adaptive and non-adaptive source identification algorithms adapted to the SICTF, even though these baseline algorithms have full access to the contact network. Extending the theory of random exponential trees, we analytically approximate the source identification probability of the LS/ LS+ algorithms, and we show that our analytic results match the simulations. Finally, we benchmark our algorithms on the Data-driven COVID-19 Simulator (DCS) developed by Lorch et al., which is the first time source identification algorithms are tested on such a complex dataset.

Exclusion of pregnant people from emergency vaccine clinical trials: A systematic review of clinical trial protocols and reporting from 2009 to 2019

BACKGROUND

Existing ethics guidance and regulatory requirements emphasize the need for pregnancy-specific safety and efficacy data during the development of vaccines in health emergencies. Our objective was to conduct a systematic review of vaccine clinical trials during active epidemic periods.

METHODS

We searched for Phase II and Phase III vaccine clinical trials initiated during the H1N1 influenza, Middle East Respiratory Syndrome Coronavirus (MERS-CoV), Zika, and Ebola virus disease (EVD) outbreaks from 2009 to 2019. Data were extracted from clinical trial protocols identified in the following registries: ClinicalTrials.gov, Pan African Clinical Trial Registry (PACTR), and all primary registries indicated by the World Health Organization's International Clinical Trials Registry Platform (ICTRP). Published studies from registered clinical trials were located through PubMed. Data was extracted on eligibility criteria and pregnancy outcomes. Data from this study is available in the Center for Open Science Data Repository: https://osf.io/nfk2p/?view_only=47deb3b206724af9b46c9c0c0083a267.

RESULTS

We identified 96 vaccine clinical trial protocols and included 84 in analysis. 5 records were excluded in screening for irrelevant abstracts, 7 were excluded in full-text assessment (1 for a therapeutic drug trial, 3 for enrolling elderly adults only, 3 for enrolling children/adolescents only). There were no eligible trials for MERS-CoV or Zika virus vaccines. Overall, 8 protocols explicitly included pregnant people; of these, 3 were completed trials with published results. Incidental pregnancies and outcomes of pregnant participants were reported in 2 studies, 10 studies reported serious adverse events related to pregnancy without mentioning total incidental pregnancies. A total of 411 recorded pregnancy outcomes were reported, with 293 from the 3 pregnancy-eligible studies with results. 71 serious adverse events pertaining to pregnancy were reported from all clinical trials with results.

CONCLUSION

Pregnant people are underrepresented in vaccine clinical trials conducted during outbreaks, resulting in underreporting of pregnancy-related outcomes and a lack of protection for pregnant people and neonates from infectious diseases.

Necessary and sufficient conditions for exact closures of epidemic equations on configuration model networks

We prove that it is possible to obtain the exact closure of SIR pairwise epidemic equations on a configuration model network if and only if the degree distribution follows a Poisson, binomial, or negative binomial distribution. The proof relies on establishing the equivalence, for these specific degree distributions, between the closed pairwise model and a dynamical survival analysis (DSA) model that was previously shown to be exact. Specifically, we demonstrate that the DSA model is equivalent to the well-known edge-based Volz model. Using this result, we also provide reductions of the closed pairwise and Volz models to a single equation that involves only susceptibles. This equation has a useful statistical interpretation in terms of times to infection. We provide some numerical examples to illustrate our results.

Strategies for maintaining and strengthening the health care workers during epidemics: a scoping review

INTRODUCTION

During epidemics such as COVID-19, healthcare workers (HCWs) face several challenges, leading to a shortage and weakening of human resources. To address this issue, employing effective strategies is essential in maintaining and strengthening human resources during outbreaks. This study aimed to gather and classify strategies that could retain and strengthen human health resources during epidemics.

METHODS

In this scoping review, all studies published about strategies for maintaining and strengthening HCWs in epidemics were collected from 4 international databases, including PubMed, Embase, Scopus, and Web of Science. The English language articles published after 2000 up until June 2022 recommended specific strategies regarding the research question. Then, they were analyzed and classified according to thematic analysis based on Braun and Clarke 6 phases protocols.

RESULTS

In total, 9405 records were screened, of which 59 articles were included, and their full texts were reviewed. Fifty factors were identified and classified into five themes: Instruction, Protection, Supporting, Caring, and Communication. Most of the suggestions were conducted in high-income countries and related to the Supporting theme.

DISCUSSION

The majority of strategies discussed in the literature addressed only one or two aspects of human resources. This study provides a holistic perspective on these issues by providing a thematic map of different strategies for strengthening and maintaining HCWs during epidemics. Considering the multidimensionality of human nature, it is suggested that policymakers and managers of health systems provide facilities that simultaneously address a wide range of needs.

[Monkeypox and men who have sex with men: we need to treat it with a destigmatizing perspective.]

Monkeypox is a zoonosis, which is transmitted by direct and close contact, and has recently produced a large epidemic outbreak in non-endemic areas, classified as a Public Health Emergency of international concern by the World Health Organization. The global doubting and delayed response, and the stigmatizing approach to men who have sex with men promoted by public opinion, some scientists, socio-political agents, and the media, could be some of the reasons why the epidemic has not yet been contained. The stigma around the disease produces severe injuries to its victims and obstructs actions aimed at controlling the disease, as occurred with HIV in the past. To limit stigma and contain the outbreak, scientists should lead the diffusion of scientifically validated information, educate the population about prevention, symptoms, how to react in case of suspicion and the importance of not contributing to others' stigmatization. Interventions must also be conducted on victims of stigma to counter act its consequences and encourage their self-efficacy. Public health interventions should be carried out in collaboration with political and social actors to integrate evidence into regulations and procedures and facilitate public health interventions. Experts should also work together with the media to facilitate proper health communication and warn about bad practices. Similarly, the relationship between organizations and health professionals and stigmatized individuals must be improved to optimize their access and retention in health systems. The objective of this study was to review the stigmatizing response of some political agents, the media and social opinion to the Monkeypox epidemic, to highlight the consequences of stigma on the patients and the control of the disease. And to establish a series of recommendations to treat this situation effectively through a non-stigmatizing approach.

Ebola and slum dwellers: Community engagement and epidemic response strategies in urban Sierra Leone

The Ebola epidemic in West Africa (2013-2016) was a learning process for all - the population, health experts and practitioners, as well as government structures. Learning occurred in all stages of the response, from the initial lack of clarity and denial of Ebola's existence that contributed to public confusion; to the eventual acceptance of the existence of the Ebola threat whereupon fear and stigmatization reigned; to the later stages in which community engagement and ownership of the response arose. In this paper we describe how two urban poor communities in informal settlements in the Western Area of Sierra Leone responded to Ebola Virus Disease and how they deployed efficient strategies like the development and implementation of by-laws for monitoring and surveillance, thus helping to curb the epidemic. For future public health emergencies, we recommend that community engagement be pursued earlier and that efforts are made to ensure two-way knowledge exchange between responders and community stakeholders.

Religion in times of epidemics, a matter of public health: great plague of Marseille (FRA, 1720-1723) Covid-19 (2020-...), a narrative review

BACKGROUND

Humans have always referred to religion in History to explain disasters, and epidemics, especially when science could not explain them. Religion has often been invoked as a mean of protection. The Covid outbreak in 2020 and the initial medical impotence brought up old fears, reminiscent of the plague for some people. Unable to rely on science only, some turned back to religion.

METHODOLOGY

A narrative review was conducted to compare the role of religion during the Great Plague of Marseille versus the early stages of Covid-19 pandemic. We mostly studied contemporary documents on the Great Plague of Marseille, and collected press articles on Covid-19.

RESULTS/DISCUSSION

For both epidemics, some people see in the outbreak a sign of God's revenge. Logically, intensifying spiritual life and multiplying religious demonstrations can be a way to fight both epidemics. Studying religion in these times of epidemics also highlights its roles in public health: sometimes facilitating the contaminations if not regulated, sometimes supporting public health policies with some positions, as for Covid vaccines. Conclusion/Perspectives: The comparison of an ancient epidemic with the current pandemic allowed us to take a broader look at the current vision of contagious disease, in societies that have become highly medicalized. The fight against epidemics remains polymorphous, and one of the aspects is religious. Integrating this information in our practices can help improving holistic management of patients, and public health policies efficiency.

Optimality of Maximal-Effort Vaccination

It is widely acknowledged that vaccinating at maximal effort in the face of an ongoing epidemic is the best strategy to minimise infections and deaths from the disease. Despite this, no one has proved that this is guaranteed to be true if the disease follows multi-group SIR (Susceptible-Infected-Recovered) dynamics. This paper provides a novel proof of this principle for the existing SIR framework, showing that the total number of deaths or infections from an epidemic is decreasing in vaccination effort. Furthermore, it presents a novel model for vaccination which assumes that vaccines assigned to a subgroup are distributed randomly to the unvaccinated population of that subgroup. It suggests, using COVID-19 data, that this more accurately captures vaccination dynamics than the model commonly found in the literature. However, as the novel model provides a strictly larger set of possible vaccination policies, the results presented in this paper hold for both models.

Need for effective detection and early warnings for epidemic and pandemic preparedness planning in the context of multi-hazards: Lessons from the COVID-19 pandemic

The need for effective early detection and timely surveillance for a robust pandemic and epidemic early warning and preparedness has been widely discussed amidst the Covid-19 pandemic, which suddenly erupted worldwide. This need is further established by various other hazards reported in many countries amidst the COVID-19 pandemic. In addition, the failure of early detection of pathogens and their source of origin has been largely connected with global transmission and severe outbreaks in many contexts. Therefore, effective early detection , timely surveillance and early warning are key aspects of a successful response to an epidemic or pandemic. . Hence, this paper aims to identify key elements and stages of an effective epidemic and pandemic early warning (EW) and response system. Further, the paper analyses inter-connections of the elements of the early warning system, focusing on the COVID-19 and multi-hazard context. The systematic literature review method was used to collect data from electronic databases. Results suggest that epidemiological surveillance & detection, primary screening of raw data & information, risk and vulnerability assessments, prediction and decision-making, alerts & early warnings are critical components of epidemic and pandemic EW. In addition, response-control & mitigation, preparedness-preventive strategies, and reducing transmission , elimination and eradication of the disease are integrated components of the early warning and response ecosystem that largely depend on effective early warnings. The significance of integrating epidemic and pandemic EW with other EWs to operate as multi-hazard early warning systems is also analysed.

Population genomics implies potential public health risk of two non-toxigenic Vibrio cholerae lineages

Diarrheal cases caused by non-toxigenic Vibrio cholerae have been reported globally. Lineages L3b and L9, characterized as ctxAB-negative and tcpA-positive (CNTP), pose the highest risk and have caused long-term epidemics in different regions worldwide. From 2001 to 2018, two waves (2001-2012 and 2013-2018) of epidemic caused by non-toxigenic V. cholerae occurred in the developed city of Hangzhou, China. In this study, through the integrated analysis of 207 genomes of Hangzhou isolates from these two waves (119 and 88) and 1573 publicly available genomes, we showed that L3b and L9 lineages together caused the second wave as had happened in the first wave, but the dominant lineage shifted from L3b (first wave: 69%) to L9 (second wave: 50%). We further found that the genotype of a key virulence gene, tcpF, in the L9 lineage during the second wave shifted to type I, which may have enhanced bacterial colonization in humans and potentially promoted the pathogenic lineage shift. Moreover, we found that 21% of L3b and L9 isolates had changed to predicted cholera toxin producers, providing evidence that gain of complete CTXφ-carrying ctxAB genes, rather than ctxAB gain in pre-CTXφ-carrying isolates, led to the transition. Taken together, our findings highlight the possible public health risk associated with L3b and L9 lineages due to their potential to cause long-term epidemics and turn into high-virulent cholera toxin producers, which necessitates a more comprehensive and unbiased sampling in further disease control efforts.

[Management of COVID-19 pandemic in hospital at home in two regional Spanish hospitals. what it meant, who we cared for, who died and how assistance has evolved over time]

OBJECTIVE

Home Hospitalization (HH) is an alternative hospitalization modality that can be very useful in times of health stress such as the COVID-19 pandemic. This paper includes the management of patients admitted with COVID-19 in HH in two county spanish hospitals for two years.

METHODS

A descriptive, observational and retrospective study of all patients admitted at HH with a diagnosis of COVID-19 disease was carried out. Subsequently, further analysis was carried out to characterize the patients who died in HH or 30 days after discharge and another to compare the management between the first phase of the study (2020) and the second one (2021 and part of 2022).

RESULTS

A total of 167 patients were recruited. A 52.1% moved to watch that the recovery continued compared to 40.7% in which it was done to watch that they did not worsen. The patients who died in HAD were older (mean 87.5 years), more comorbid and more likely to have do-not resucitate orders (DNR) in case of cardiac arrest (85%). In the second phase of the study, older patients, more comorbid patients and with a greater degree of DNR orders were admitted than those admitted throughout 2020.

CONCLUSIONS

HAD is a useful resource to increase the resilience of health systems in cases of stress such as the disease caused by COVID-19. The development and growth of existing units, as well as the creation of new ones where they do not exist, could be a basic tool for the medicine of the future.

The Surviral Epidemiological Early Warning System Tyrol Dashboard - System, Functionality and Findings

BACKGROUND

Dashboards provide a good retrospective view of the development of the disease. Yet, current COVID-related dashboards typically lack the capability to predict future trends. However, this is important for health policy makers and health care providers in order to adopt meaningful containment strategies.

OBJECTIVES

The aim of this paper is to present the Surviral dashboard, which allows the effective monitoring of infectious disease dynamics.

METHODS

The presented dashboard comprises a wide range of information, including retrospective and prognostic data based on an agent-based simulation framework. It served as the basis for informed decision-making and planning of disease control strategies within the federal state of Tyrol.

RESULTS

By visualizing the information in an understandable format, the dashboard provided a comprehensive overview of the COVID-19 situation in Tyrol and allowed for the identification of trends and patterns.

CONCLUSION

The presented dashboard is a valuable tool for managing pandemics such as COVID-19. It provides a convenient and efficient way to monitor the spread of a disease and identify potential areas for intervention.

Mental health and wellbeing of health and aged care workers in Australia, May 2021 - June 2022: a longitudinal cohort study

OBJECTIVES

To assess the mental health and wellbeing of health and aged care workers in Australia during the second and third years of the coronavirus disease 2019 (COVID-19) pandemic, overall and by occupation group.

DESIGN, SETTING, PARTICIPANTS

Longitudinal cohort study of health and aged care workers (ambulance, hospitals, primary care, residential aged care) in Victoria: May-July 2021 (survey 1), October-December 2021 (survey 2), and May-June 2022 (survey 3).

MAIN OUTCOME MEASURES

Proportions of respondents (adjusted for age, gender, socio-economic status) reporting moderate to severe symptoms of depression (Patient Health Questionnaire-9, PHQ-9), anxiety (Generalized Anxiety Disorder scale, GAD-7), or post-traumatic stress (Impact of Event Scale-6, IES-6), burnout (abbreviated Maslach Burnout Inventory, aMBI), or high optimism (10-point visual analogue scale); mean scores (adjusted for age, gender, socio-economic status) for wellbeing (Personal Wellbeing Index-Adult, PWI-A) and resilience (Connor Davidson Resilience Scale 2, CD-RISC-2).

RESULTS

A total of 1667 people responded to at least one survey (survey 1, 989; survey 2, 1153; survey 3, 993; response rate, 3.3%). Overall, 1211 survey responses were from women (72.6%); most respondents were hospital workers (1289, 77.3%) or ambulance staff (315, 18.9%). The adjusted proportions of respondents who reported moderate to severe symptoms of depression (survey 1, 16.4%; survey 2, 22.6%; survey 3, 19.2%), anxiety (survey 1, 8.8%; survey 2, 16.0%; survey 3, 11.0%), or post-traumatic stress (survey 1, 14.6%; survey 2, 35.1%; survey 3, 14.9%) were each largest for survey 2. The adjusted proportions of participants who reported moderate to severe symptoms of burnout were higher in surveys 2 and 3 than in survey 1, and the proportions who reported high optimism were smaller in surveys 2 and 3 than in survey 1. Adjusted mean scores for wellbeing and resilience were similar at surveys 2 and 3 and lower than at survey 1. The magnitude but not the patterns of change differed by occupation group.

CONCLUSION

Burnout was more frequently reported and mean wellbeing and resilience scores were lower in mid-2022 than in mid-2021 for Victorian health and aged care workers who participated in our study. Evidence-based mental health and wellbeing programs for workers in health care organisations are needed.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry: ACTRN12621000533897 (observational study; retrospective).

Progress towards dog-mediated rabies elimination in PR China: a scoping review

BACKGROUND

Rabies continues to be a serious threat to global public health endangering people's health and public health safety. In the People's Republic of China, multi-sectoral and comprehensive prevention and control strategies have aimed to extensively curb human rabies transmission. Here, we examine the current state of rabies infection in China, explore strategic interventions put in place in response to WHO's ambition of "Zero rabies deaths by 2030" and critically assess the constraints and feasibility of dog-mediated rabies elimination in China.

METHODS

This study analyzed and evaluated the process towards dog-mediated rabies elimination in China from five perspectives: namely, human, dog, policy, challenge, and prospects. Evidence-based data on progress of dog-mediated rabies elimination in China was derived from a number of sources; a literature search was undertaken using PubMed, Web of Science and CNKI databases, distribution data for human rabies cases as derived from the Data-center of the China Public Health Science and policy and document data were obtained from official websites of the relevant China ministries and commissions.

RESULTS

The incidence of human rabies cases in China have shown a downward trend year-on-year since 2007. Implementation of a government-led, multi-sectoral "One Health" approach to combating rabies has driven down the total number of rabies deaths nationwide to around 200 in 2020. The number of provincial-level administrative divisions (PLADs) reporting human cases of rabies has also decreased to 21 in 2020, 13 of which reported less than 10 cases. Furthermore, the number of outpatient visits seeking rabies post-exposure prophylaxis has risen dramatically over the past two decades, with demand being 15 times higher than it was initially. There remain however, significant gaps in rabies elimination outcomes across the different regions of China. To date the target of achieving a canine rabies vaccination rate of > 75% has not been met. The challenges of rabies immunization of dogs and dog management in underdeveloped cities and rural areas need to be addressed together with more effective animal surveillance and rabies risk from and too wildlife and livestock.

CONCLUSIONS

The Chinese government-led, multi-sectoral "One Health" approach to combating rabies and has made significant progress over the past decade. Development and adoption of more cost-effective One Health strategies can achieve more nationally beneficial rabies elimination outcomes. The ambitious target of "Zero rabies deaths by 2030" can be met through establishment of long-lasting herd immunity in dogs by means of dog mass vaccination campaigns, dog population management, epidemiological surveillance and the application of large-scale oral rabies vaccine to eliminate rabies in wild animals coupled with deployment of cost-effective human post-exposure prophylaxis, and community education.