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In past decades, we have seen several epidemics of respiratory infections from newly emerging viruses, most of which originated in animals. These emerging infections, including severe acute respiratory syndrome coronavirus (SARS‐CoV), Middle East respiratory syndrome coronavirus (MERS‐CoV) and the pandemic influenza A(H1N1) and avian influenza (AI) viruses, have seriously threatened global health and the economy. In particular, MERS‐CoV and AI A(H7N9) are still causing infections in several areas, and some clustering of cases of A(H5N1) and A(H7N9) may imply future possible pandemics. Additionally, given the inappropriate use of antibiotics and international travel, the spread of carbapenem‐resistant Gram‐negative bacteria is also a significant concern. These infections with epidemic or pandemic potential present a persistent threat to public health and a huge burden on healthcare services in the Asia‐Pacific region. Therefore, to enable efficient infection prevention and control, more effective international surveillance and collaboration systems, in the context of the ‘One Health’ approach, are necessary.

A world without HIV is only possible by addressing the socio-structural determinants of health. Our understanding of socio-structural determinants is constantly changing, and parallel changes must occur with the methodologies used to explain the drivers of the HIV epidemic. We argue for the need to engage communities in the planning, implementation, and dissemination of research on the socio-structural determinants of HIV. Community engagement should cross-cut various types of research including rigorous measurement development of socio-structural determinants and novel analytic techniques to model their role in the trajectory of the epidemic and the impact of interventions. Considering the role of place, we recommend collaboration between scientists and communities in the interpretation of results from studies that map HIV-related behaviors and movement. As we collectively delve into historically oppressive systems with colonial antecedents, we must be ready to challenge these systems and replace them with collaborative models. The success of research-driven HIV policy and programming will best be evaluated with methodologies derived from the insights of the very individuals that these policies and programs aim to serve.Un mundo sin VIH es posible sólo si atendemos los determinantes socio-estructurales de la salud. Nuestra comprensión sobre determinantes socio-estructurales cambia constantemente y cambios similares deben ocurrir en las metodologías utilizadas para explicar los factores que rigen la epidemia del VIH. Argumentamos sobre la necesidad de involucrar las comunidades en la planificación, implementación y diseminación de investigaciones sobre los determinantes socio-estructurales del VIH. La participación comunitaria debe ser transversal en varios tipos de investigaciones, incluyendo el desarrollo riguroso de métricas sobre los determinantes socio-estructurales y técnicas noveles para la modelación de su rol en las trayectorias de la epidemia y el impacto de intervenciones. Considerando el rol que tiene el lugar físico, recomendamos la colaboración de científicos y comunidades en la interpretación de resultados de estudios que crean mapas de las conductas relacionadas al VIH y la movilidad de las personas. En la medida en que examinamos sistemas históricamente opresivos con antecedentes coloniales, debemos estar listos para retar estos sistemas y remplazarlos con modelos colaborativos. El logro de políticas y programas de VIH informados por la investigación sería evaluado mejor si se utilizan metodologías guiadas por el conocimiento de las personas a las cuales estas políticas y programas persiguen servir.

Tijuana, Mexico, has a concentrated HIV epidemic among overlapping key populations (KPs) including people who inject drugs (PWID), female sex workers (FSW), their male clients, and men who have sex with men (MSM). We developed a dynamic HIV transmission model among these KPs to determine the extent to which their unmet prevention and treatment needs is driving HIV transmission. Over 2020-2029 we estimated the proportion of new infections acquired in each KP, and the proportion due to their unprotected risk behaviours. We estimate that 43.7% and 55.3% of new infections are among MSM and PWID, respectively, with FSW and their clients making-up 10% of new infections. Projections suggest 93.8% of new infections over 2020-2029 will be due to unprotected sex between MSM or unsafe injecting drug use. Prioritizing interventions addressing sexual and injecting risks among MSM and PWID are critical to controlling HIV in Tijuana.Tijuana, México, tiene una epidemia de VIH concentrada en poblaciones claves (PC) superpuestas que incluyen personas que se inyectan drogas (PID), trabajadoras sexuales (MTS), sus clientes hombres, y hombres que tienen sexo con hombres (HSH). Desarrollamos un modelo dinámico de transmisión de VIH en estas PC para determinar hasta dónde sus necesidades no atendidas de prevención y tratamiento dirigen la transmisión del VIH. Para 2020–2029 estimamos la proporción de nuevas infecciones adquiridas en cada PC, y la proporción atribuida a sus comportamientos de riesgo sin protección. Estimamos que 43.7% y 55.3% de nuevas infecciones se dan en HSH y PID, respectivamente, con MTS y clientes conformando 10% de nuevas infecciones. Las proyecciones sugieren que 93.8% de nuevas infecciones en 2020–2029 se deberán a sexo sin protección en HSH o uso inseguro de drogas inyectables. Dar prioridad a intervenciones que atienden los riesgos sexual y de inyección en HSH y PID es crítico para controlar el VIH en Tijuana.

Facing with COVID-19 epidemic such a catastrophic health emergency, the mental health status of medical staff deserves attention. We conducted a two-stage of psychological status monitoring after the end of the assistance and 14 days of isolation, further targeted the vulnerable groups in need of intervention. The study is a cross-sectional survey on 1156 Yunnan medical staff aid to Hubei. Used Cluster sampling method to collect data at 2 time points (at the end of returning from Wuhan and the 14th day of isolation), from March 18, 2020 to April 6, 2020. Female and nurse had higher rates of depressive symptoms than male and doctors and other occupations. The proportion of female with mild and above moderate anxiety levels (22.91%, 2.61%) was higher than male (17.35%, 1.03%) (p

Background The outbreak of coronavirus disease 2019 (COVID‐19) rapidly spread across worldwide, posing a significant challenge to public health. Several shortcomings in the existing infectious disease management system were exposed during the pandemic, which hindered the control of the disease globally. To cope with this issue, we propose a window‐period framework to reveal the general rule of the progression of management of infectious diseases and to help with decision making at the early stage of epidemics with a focus on healthcare provisions. Methods The framework has two significant periods (dark‐window period and bright‐window period). Outbreak of COVID‐19 in China was used as an example for the application of the framework. Results The framework could reflect the progression of the epidemic objectively. The spread increased slowly in the dark‐window period, but rocketed up in the bright‐window period. The beginning of the bright‐window period was the time when healthcare personnel were exposed to a substantially high risk of nosocomial infection. Additionally, proper and prompt preventive actions during the dark‐window and bright‐window periods were substantially important to reduce the future spreading of the disease. Conclusions It was recommended that when possible healthcare provisions should upgrade to the highest level of alert for the control of an unknown epidemic in the dark‐window period, while countermeasures in the bright‐window period could be accordingly adjusted with full exploration and considerations. The framework may provide some insights into how to accelerate the control of future epidemics promptly and effectively.

Epidemics such as novel Coronavirus 2019 (COVID-19) can be contained and the rate of infection reduced by public health measures such as epidemiologic inquiries and social distancing. Epidemiologic inquiry requires resources and time which may not be available or reduced when the outbreak is excessive. We evaluated the use of Google Maps Timeline (GMTL) for creating spatial epidemiologic timelines. The study compares locations, routes, and means of transport between GMTL and user recall for 17 suitable users who were recruited during March 2020. They were interviewed about their timeline using the Timeline Follow-Back (TLFB) method which was then compared to their GMTL and discrepancies between both methods were addressed. Interviewer conclusions were divided into categories: (1) participant recalled, (2) no recall (until shown). Categories were subdivided by GMTL accuracy: [a] GMTL accurate, [b] GMTL inaccurate, [c] GMTL data missing. A total of 362 locations were compared. Participants recalled 322 (88.95% SD = 8.55) locations compared with 40 (11.05%, SD = 2.05) locations not recalled. There were 304 locations found accurate on GMTL (83.98%, SD = 9.49), 29 (8.01%, SD = 1.11) inaccurate locations, and 29 (8.01%, SD = 0.54) missing locations. The total discrepancy between GMTL and TLFB recall was 95 cases (26.24%, SD = 3.25). Despite variations between users, Google Maps with GMTL technology may be useful in identifying potentially exposed individuals in a pandemic. It is especially useful when resources are limited. Further research is required with a larger number of users who are undergoing a real epidemiologic investigation to corroborate findings and establish further recommendations.

Surprisingly, Catholic hagiography can teach us a lot about medicine. As an example, we present here the history of Saint Roch who is considered, along with Saint Sebastian, one of the two main Saints who act as protectors against plagues and have often been invoked by Catholic people during the recent COVID-19 pandemic. Whereas Saint Sebastian is associated with plagues only symbolically, Catholic tradition considers Saint Roch to have had real contact with the bubonic plague. Indeed, during his pilgrimage to Rome, Saint Roch helped people suffering from the plague and was, subsequently, himself infected. He was then forced to retire in solitary confinement to avoid the plague’s spread and followed scrupulously the necessary restrictive measures. Saint Roch’s story provides useful lessons about the importance of social responsibility by respecting the restrictive rules during difficult times such as the COVID-19 epidemic.

During the coronavirus disease 2019 (COVID-19) pandemic, many countries opted for strict public health measures, including closing schools. After some time, they have started relaxing some of those restrictions. To avoid overwhelming health systems, predictions for the number of new COVID-19 cases need to be considered when choosing a school reopening strategy. Using a computer simulation based on a stochastic compartmental model that includes a heterogeneous and dynamic network, we analyse different strategies to reopen schools in the São Paulo Metropolitan Area, including one similar to the official reopening plan. Our model allows us to describe different types of relations between people, each type with a different infectiousness. Based on our simulations and model assumptions, our results indicate that reopening schools with all students at once has a big impact on the number of new COVID-19 cases, which could cause a collapse of the health system. On the other hand, our results also show that a controlled school reopening could possibly avoid the collapse of the health system, depending on how people follow sanitary measures. We estimate that postponing the schools' return date for after a vaccine becomes available may save tens of thousands of lives just in the São Paulo Metropolitan Area compared to a controlled reopening considering a worst-case scenario. We also discuss our model constraints and the uncertainty of its parameters.

BackgroundObtaining comprehensive epidemic information for specific global infectious diseases is crucial to travel health. However, different infectious disease information websites may have different purposes, which may lead to misunderstanding by travelers and travel health staff when making accurate epidemic control and management decisions.ObjectiveThe objective of this study was to develop a Global Infectious Diseases Epidemic Information Monitoring System (GIDEIMS) in order to provide comprehensive and timely global epidemic information.MethodsDistributed web crawler and cloud agent acceleration technologies were used to automatically collect epidemic information about more than 200 infectious diseases from 26 established epidemic websites and Baidu News. Natural language processing and in-depth learning technologies have been utilized to intelligently process epidemic information collected in 28 languages. Currently, the GIDEIMS presents world epidemic information using a geographical map, including date, disease name, reported cases in different countries, and the epidemic situation in China. In order to make a practical assessment of the GIDEIMS, we compared infectious disease data collected from the GIDEIMS and other websites on July 16, 2019.ResultsCompared with the Global Incident Map and Outbreak News Today, the GIDEIMS provided more comprehensive information on human infectious diseases. The GIDEIMS is currently used in the Health Quarantine Department of Shenzhen Customs District (Shenzhen, China) and was recommended to the Health Quarantine Administrative Department of the General Administration of Customs (China) and travel health–related departments.ConclusionsThe GIDEIMS is one of the most intelligent tools that contributes to safeguarding the health of travelers, controlling infectious disease epidemics, and effectively managing public health in China.

Estimating the case fatality ratio (CFR) for COVID-19 is an important aspect of public health. However, calculating CFR accurately is problematic early in a novel disease outbreak, due to uncertainties regarding the time course of disease and difficulties in diagnosis and reporting of cases. In this work, we present a simple method for calculating the case fatality ratio using only public case and death data over time by exploiting the correspondence between the time distributions of cases and deaths. The time-shifted distribution (TSD) analysis generates two parameters of interest: the delay time between reporting of cases and deaths and the case fatality ratio. These parameters converge reliably over time once the exponential growth phase has finished. Analysis is performed for early COVID-19 outbreaks in many countries, and we discuss corrections to CFR values using excess-death and seroprevalence data to estimate the infection fatality ratio (IFR). While CFR values range from 0.2-20% in different countries, estimates for IFR are mostly around 0.5-0.8% for countries that experienced moderate outbreaks and 1-3% for severe outbreaks. The simplicity and transparency of TSD analysis enhance its usefulness in characterizing a new disease as well as the state of the health and reporting systems.

Background On March 9, 2020, the first COVID-19 case was reported in Jodhpur, Rajasthan, in the northwestern part of India. Understanding the epidemiology of COVID-19 at a local level is becoming increasingly important to guide measures to control the pandemic. Objective The aim of this study was to estimate the serial interval and basic reproduction number (R0) to understand the transmission dynamics of the COVID-19 outbreak at a district level. We used standard mathematical modeling approaches to assess the utility of these factors in determining the effectiveness of COVID-19 responses and projecting the size of the epidemic. Methods Contact tracing of individuals infected with SARS-CoV-2 was performed to obtain the serial intervals. The median and 95th percentile values of the SARS-CoV-2 serial interval were obtained from the best fits with the weibull, log-normal, log-logistic, gamma, and generalized gamma distributions. Aggregate and instantaneous R0 values were derived with different methods using the EarlyR and EpiEstim packages in R software. Results The median and 95th percentile values of the serial interval were 5.23 days (95% CI 4.72-5.79) and 13.20 days (95% CI 10.90-18.18), respectively. R0 during the first 30 days of the outbreak was 1.62 (95% CI 1.07-2.17), which subsequently decreased to 1.15 (95% CI 1.09-1.21). The peak instantaneous R0 values obtained using a Poisson process developed by Jombert et al were 6.53 (95% CI 2.12-13.38) and 3.43 (95% CI 1.71-5.74) for sliding time windows of 7 and 14 days, respectively. The peak R0 values obtained using the method by Wallinga and Teunis were 2.96 (95% CI 2.52-3.36) and 2.92 (95% CI 2.65-3.22) for sliding time windows of 7 and 14 days, respectively. R0 values of 1.21 (95% CI 1.09-1.34) and 1.12 (95% CI 1.03-1.21) for the 7- and 14-day sliding time windows, respectively, were obtained on July 6, 2020, using method by Jombert et al. Using the method by Wallinga and Teunis, values of 0.32 (95% CI 0.27-0.36) and 0.61 (95% CI 0.58-0.63) were obtained for the 7- and 14-day sliding time windows, respectively. The projection of cases over the next month was 2131 (95% CI 1799-2462). Reductions of transmission by 25% and 50% corresponding to reasonable and aggressive control measures could lead to 58.7% and 84.0% reductions in epidemic size, respectively. Conclusions The projected transmission reductions indicate that strengthening control measures could lead to proportionate reductions of the size of the COVID-19 epidemic. Time-dependent instantaneous R0 estimation based on the process by Jombart et al was found to be better suited for guiding COVID-19 response at the district level than overall R0 or instantaneous R0 estimation by the Wallinga and Teunis method. A data-driven approach at the local level is proposed to be useful in guiding public health strategy and surge capacity planning.

Background Several countries adopted lockdown to slowdown the exponential transmission of the coronavirus disease (COVID-19) epidemic. Disease transmission models and the epidemic forecasts at the national level steer the policy to implement appropriate intervention strategies and budgeting. However, it is critical to design a data-driven reliable model for nowcasting for smaller populations, in particular metro cities. Objective The aim of this study is to analyze the transition of the epidemic from subexponential to exponential transmission in the Chennai metro zone and to analyze the probability of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) secondary infections while availing the public transport systems in the city. Methods A single geographical zone “Chennai-Metro-Merge” was constructed by combining Chennai District with three bordering districts. Subexponential and exponential models were developed to analyze and predict the progression of the COVID-19 epidemic. Probabilistic models were applied to assess the probability of secondary infections while availing public transport after the release of the lockdown. Results The model predicted that transition from subexponential to exponential transmission occurs around the eighth week after the reporting of a cluster of cases. The probability of secondary infections with a single index case in an enclosure of the city bus, the suburban train general coach, and the ladies coach was found to be 0.192, 0.074, and 0.114, respectively. Conclusions Nowcasting at the early stage of the epidemic predicts the probable time point of the exponential transmission and alerts the public health system. After the lockdown release, public transportation will be the major source of SARS-CoV-2 transmission in metro cities, and appropriate strategies based on nowcasting are needed.

Following the importation of coronavirus disease (COVID-19) into Nigeria on 27 February 2020 and then the outbreak, the question is: How do we anticipate the progression of the ongoing epidemic following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population. In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of COVID-19 in Nigeria to estimate the effective reproduction number (R(t)) and basic reproduction number (R0) – this also enables us to estimate the initial daily transmission rate (β0). We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on COVID-19 symptomatic and death cases from 27 February 2020 and 7 May 2020. In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 2.29. Most importantly, the R(t) is strictly greater than one from 13 April till 7 May 2020. The R0 is estimated to be 2.42 with credible interval: (2.37–2.47). Comparing this with the R(t) shows that control measures are working but not effective enough to keep R(t) below 1. Also, the estimated fraction of reported symptomatic cases is between 10 and 50%. Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed.

Objective:To investigate the knowledge, attitudes and practices (K-A-P) about food safety and nutrition in Chinese adults who were recruited to the online survey during the epidemic of corona virus disease 2019 (COVID-19).Design:Participants were recruited by an online snowball sampling method. An electronic questionnaire was sent to our colleagues, students, friends, other professionals and their referrals helped us recruit more participants. The questionnaire included socio-demographic information, the attention paid to COVID-19, K-A-P about food safety and nutrition. Multiple and logistic regression analyses were used to explore related factors of K-A-P.Subjects:Totally, 2272 participants aged 24·09 ± 9·14 years, from twenty-seven provinces, autonomous districts or municipalities, with 18·3 % male and 83·4 % with a medical background.Results:The total possible knowledge score was 8·0, the average score was 5·2 ± 1·6 and 4·2 % obtained 8·0. The total possible attitudes score was 8·0, the average score was 6·5 ± 1·4 and 36·1 % obtained 8·0. The total possible food safety practices score was 5·0, the average score was 3·7 ± 1·0 and 20·7 % obtained 5·0. During this public emergency, 79·4 % participants changed diet habits, including increasing vegetables, fruit and water intake and reducing sugary drinks and snacks. Gender, age, educational and professional background, disease history, the attention paid to COVID-19 and related knowledge were associated with K-A-P.Conclusion:There was room for the improvement of K-A-P in participants during this public health emergency and further strengthening education about food safety and nutrition is needed. Findings indicate that education should address biased or misleading information and promote nutritious food choices and safe food practices.

Background Noncommunicable diseases (NCDs) have become the main public health concern worldwide. With rapid economic development and changes in lifestyles, the burden of NCDs in China is increasing dramatically every year. Monitoring is a critical measure for NCDs control and prevention. However, because of the lack of regional representativeness, unsatisfactory data quality, and inefficient data sharing and utilization, the existing surveillance systems and surveys in China cannot track the status and transition of NCDs epidemic. Objective To efficaciously track NCDs epidemic in China, this pilot program conducted in Ningbo city by the Chinese Center for Disease Control and Prevention (CDC) aimed to develop an innovative model for NCDs surveillance and management: the integrated noncommunicable disease collaborative management system (NCDCMS). Methods This Ningbo model was designed and developed through a 3-level (county/district, municipal, and provincial levels) direct reporting system based on the regional health information platform. The uniform data standards and interface specifications were established to connect different platforms and conduct data exchanges. The performance of the system was evaluated based on the 9 attributes of surveillance system evaluation framework recommended by the US CDC. Results NCDCMS allows automatic NCDs data exchanging and sharing via a 3-level public health data exchange platform in China. It currently covers 201 medical institutions throughout the city. Compared with previous systems, automatic popping up of the report card, automatic patient information extraction, and real-time data exchange process have highly improved the simplicity and timeliness of the system. The data quality meets the requirements to monitor the incidence trend of NCDs accurately, and the comprehensive data types obtained from the database (ie, directly from the 3-level platform on the data warehouse) also provide a useful information to conduct scientific studies. So far, 98.1% (201/205) of medical institutions across Ningbo having been involved in data exchanges with the model. Evaluations of the system performance showed that NCDCMS has high levels of simplicity, data quality, acceptability, representativeness, and timeliness. Conclusions NCDCMS completely reshaped the process of NCD surveillance reporting and had unique advantages, which include reducing the work burden of different stakeholders by data sharing and exchange, eliminating unnecessary redundancies, reducing the amount of underreporting, and structuring population-based cohorts. The Ningbo model will be gradually promoted elsewhere following this success of the pilot project, and is expected to be a milestone in NCDs surveillance, control, and prevention in China.

COVID-19 has joined the long list of human disorders with sexually dimorphic expression. Increased lethality in men was evident in the first large reports from ChinaCDC and WHO-China, and the gender gap appeared even wider in the early Italian outbreak. Newspapers and scientific journals alike have commented on this finding and the preexisting conditions, biological processes, and gender role behavior differences that may underlie it. However, as for other diseases, and in spite of years of advocating for the collection of raw epidemiological data and the analysis of clinical trial data sets by sex, very little appeared to be released about sex differences in characteristics of the epidemics beyond infection and death rates, such as severity of disease, comorbidities, rate of recovery, length of hospital stay, or number of tests for the SARS-CoV-2 coronavirus. These data are critical not only for scientists to understand the pathophysiology of disease, but also to inform decision-making by countries and healthcare systems on how to prioritize testing and best allocate scarce resources and relief funds.Systematic analysis of official websites for the 20 countries and 6 US states reporting the highest number of cases on March 21, 2020, revealed a wide disparity in sex-disaggregated data made available to the public and scholars. Only a handful of the countries reported cases by sex separately. None of the other characteristics, including fatality rates, were stratified by sex at the time. Beyond suboptimal sex disaggregation, our analysis found a paucity of usable raw data sets and a generalized lack of standardization of captured data, making comparisons difficult. A second round of data capture in April found more complete, but even more disparate, information.Our analysis revealed a wide range of sex ratios among confirmed cases, which changed over time. In countries where a male-biased sex ratio was initially reported, the reported proportion of women among cases dramatically increased in under 3 weeks. In contrast, men were consistently over-represented in severe cases, intensive care admissions, and deaths. We also show that the sex ratio varies with age, with a complex pattern, reproduced across the 6 countries for which data were found.Accurate, peer-reviewed, statistical analysis of harmonized, sex-disaggregated data for other characteristics of epidemics, such as availability of testing, suspected source of infection, or comorbidities will be critical to understand where the observed disparities come from and to generate evidence-based recommendations for decision-making by institutions and governments around the world.

BackgroundDuring the spread of the novel coronavirus disease (COVID-19), internet hospitals in China were engaged with epidemic prevention and control, offering epidemic-related online services and medical support to the public.ObjectiveThe aim of this study is to explore the role of internet hospitals during the prevention and control of the COVID-19 outbreak in China.MethodsOnline epidemic-related consultations from multicenter internet hospitals in China during the COVID-19 epidemic were collected. The counselees were described and classified into seven type groups. Symptoms were recorded and compared with reported patients with COVID-19. Hypochondriacal suspicion and offline visit motivation were detected within each counselees’ group to evaluate the social panic of the epidemic along with the consequent medical-seeking behaviors. The counselees’ motivation and the doctors’ recommendation for an offline visit were compared. Risk factors affecting the counselees’ tendency of hypochondriacal suspicion and offline visit motivation were explored by logistic regression models. The epidemic prevention and control measures based on internet hospitals were listed, and the corresponding effects were discussed.ResultsA total of 4913 consultations were enrolled for analysis with the median age of the counselees at 28 years (IQR 22-33 years). There were 104 (2.12%) healthy counselees, 147 (2.99%) hypochondriacal counselees, 34 (0.69%) exposed counselees, 853 (17.36%) mildly suspicious counselees, 42 (0.85%) moderately suspicious counselees, 3550 (72.26%) highly suspicious counselees, and 183 (3.72%) severely suspicious counselees. A total of 94.20% (n=4628) of counselees had epidemic-related symptoms with a distribution similar to those of COVID-19. The hypochondriacal suspicion (n=2167, 44.11%) was common. The counselees’ motivation and the doctors’ recommendation for offline visits were inconsistent (PConclusionsInternet hospitals can serve different types of epidemic counselees, offer essential medical supports to the public during the COVID-19 outbreak, reduce the social panic, promote social distancing, enhance the public’s ability of self-protection, correct improper medical-seeking behaviors, reduce the chance of nosocomial cross-infection, and facilitate epidemiological screening, thus, playing an important role on preventing and controlling COVID-19.

Receptive anal intercourse (RAI) carries a greater per-act risk of HIV acquisition than receptive vaginal intercourse (RVI) and may influence HIV epidemics driven by heterosexual sex. This systematic review explores the association between RAI and incident HIV among women, globally. We searched Embase and Medline through September 2018 for longitudinal studies reporting crude (cRR) or adjusted (aRR) relative risks of HIV acquisition by RAI practice among women. Of 27,563 articles identified, 17 eligible studies were included. We pooled independent study estimates using random-effects models. Women reporting RAI were more likely to acquire HIV than women not reporting RAI (pooled cRR = 1.56 95% CI 1.03–2.38, N = 18, I2 = 72%; pooled aRR = 2.23, 1.01–4.92, N = 5, I2 = 70%). In subgroup analyses the association was lower for women in Africa (pooled cRR = 1.16, N = 13, I2 = 21%) than outside Africa (pooled cRR = 4.10, N = 5, I2 = 79%) and for high-risk (pooled aRR = 1.69, N = 4, I2 = 63%) than general-risk women (pooled aRR = 8.50, N = 1). Interview method slightly influenced cRR estimates (p value = 0.04). In leave-one-out sensitivity analyses pooled estimates were generally robust to removing individual study estimates. Main limitations included poor exposure definition, incomplete adjustment for confounders, particularly condom use, and use of non-confidential interview methods. More and better data are needed to explain differences in risk by world region and risk population. Women require better counselling and greater choice in prevention modalities that are effective during RVI and RAI. Electronic supplementary material The online version of this article (10.1007/s10461-019-02651-0) contains supplementary material, which is available to authorized users.

This study aimed to analyze main groups accused on social media of causing or spreading the 2014-2016 Ebola epidemic in West Africa. In this analysis, blame is construed as a vehicle of meaning through which the lay public makes sense of an epidemic, and through which certain classes of people become "figures of blame". Data was collected from Twitter and Facebook using key word extraction, then categorized thematically. Our findings indicate an overall proximate blame tendency: blame was typically cast on "near-by" figures, namely national governments, and less so on "distant" figures, such as generalized figures of otherness ("Africans", global health authorities, global elites). Our results also suggest an evolution of online blame. In the early stage of the epidemic, blame directed at the affected populations was more prominent. However, during the peak of the outbreak, the increasingly perceived threat of inter-continental spread was accompanied by a progressively proximal blame tendency, directed at figures with whom the social media users had pre-existing biopolitical frustrations. Our study proposes that pro-active and on-going analysis of blame circulating in social media can usefully help to guide communications strategies, making them more responsive to public perceptions.

Background The Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) is a secure web-based tool that enables health care practitioners to monitor health indicators of public health importance for the detection and tracking of disease outbreaks, consequences of severe weather, and other events of concern. The ESSENCE concept began in an internally funded project at the Johns Hopkins University Applied Physics Laboratory, advanced with funding from the State of Maryland, and broadened in 1999 as a collaboration with the Walter Reed Army Institute for Research. Versions of the system have been further developed by Johns Hopkins University Applied Physics Laboratory in multiple military and civilian programs for the timely detection and tracking of health threats. Objective This study aims to describe the components and development of a biosurveillance system increasingly coordinating all-hazards health surveillance and infectious disease monitoring among large and small health departments, to list the key features and lessons learned in the growth of this system, and to describe the range of initiatives and accomplishments of local epidemiologists using it. Methods The features of ESSENCE include spatial and temporal statistical alerting, custom querying, user-defined alert notifications, geographical mapping, remote data capture, and event communications. To expedite visualization, configurable and interactive modes of data stratification and filtering, graphical and tabular customization, user preference management, and sharing features allow users to query data and view geographic representations, time series and data details pages, and reports. These features allow ESSENCE users to gather and organize the resulting wealth of information into a coherent view of population health status and communicate findings among users. Results The resulting broad utility, applicability, and adaptability of this system led to the adoption of ESSENCE by the Centers for Disease Control and Prevention, numerous state and local health departments, and the Department of Defense, both nationally and globally. The open-source version of Suite for Automated Global Electronic bioSurveillance is available for global, resource-limited settings. Resourceful users of the US National Syndromic Surveillance Program ESSENCE have applied it to the surveillance of infectious diseases, severe weather and natural disaster events, mass gatherings, chronic diseases and mental health, and injury and substance abuse. Conclusions With emerging high-consequence communicable diseases and other health conditions, the continued user requirement–driven enhancements of ESSENCE demonstrate an adaptable disease surveillance capability focused on the everyday needs of public health. The challenge of a live system for widely distributed users with multiple different data sources and high throughput requirements has driven a novel, evolving architecture design.

Since 2001 the UNAIDS Secretariat has retained the responsibility for monitoring progress towards global commitments on HIV/AIDS. Key critical characteristics of the reporting system were assessed for the reporting period from 2004 to 2014 and analyses were undertaken of response rates and core indicator performance. Country submission rates ranged from 102 (53%) Member States in 2004 to 186 (96%) in 2012. There was great variance in response rates for specific indicators, with the highest response rates for treatment-related indicators. The Global AIDS reporting system has improved substantially over time and has provided key trend data on responses to the HIV epidemic, serving as the global accountability mechanism and providing reference data on the global AIDS response. It will be critical that reporting systems continue to evolve to support the monitoring of the Sustainable Development Goals, in view of ending the AIDS epidemic as a public health threat by 2030. Electronic supplementary material The online version of this article (doi:10.1007/s10461-016-1662-9) contains supplementary material, which is available to authorized users.

Introduction: Alimentary toxoinfections represent a significant public health problem. Globalization of the market and food production, significant impoverishment of a large part of the population, and traditional approach with food preparation and consumption, cause a significant increase in the rates of population infections around the world. The epidemiological surveillance of the illness occurrence plays a significant role in monitoring and controlling the populationÂ’s burden of diseases caused by unhygienically prepared and stored food. Aim: The aim of the article is to determine the rates and trends of food related diseases in the Federation of Bosnia and Herzegovina. Results: The results of the analysis have shown that the rate of illness in the Federation of Bosnia and Herzegovina is declining but is still significantly higher than in developed western countries. Particularly great burden is on the population of the Zenica-Doboj Canton (ZDC), which can be due to the traditional relation to the preparation and storage of food, as well as to the relatively poorer economic situation in ZDC. Conclusion: We can conclude that the strengthening of the monitoring system, laboratory capacities, the availability of monitoring guides will enable responsible FBiH/Bosnia and Herzegovina institutions to better control and implement safer food practice.

Epidemics such as viral haemorrhagic fevers, severe acute respiratory syndrome, Middle East respiratory syndrome coronavirus or yet unknown ones have few chances of disappearing. Globalization, worldwide travel, climate change, social conflicts and wars, among others, are likely to favor the emergence of epidemics. Preparedness of hospitals to prevent the spread of these outbreaks is among the prioritized political programmes of many countries. The EuroNHID network has in the past drawn a map of features and equipment of hospitals across Europe to take care of highly contagious patients. We update the data regarding isolation capabilities and recommendations, with an emphasis on Mediterranean countries., peer-reviewed

Background Throughout March 2020, leaders in countries across the world were making crucial decisions about how and when to implement public health interventions to combat the coronavirus disease (COVID-19). They urgently needed tools to help them to explore what will work best in their specific circumstances of epidemic size and spread, and feasible intervention scenarios. Objective We sought to rapidly develop a flexible, freely available simulation model for use by modelers and researchers to allow investigation of how various public health interventions implemented at various time points might change the shape of the COVID-19 epidemic curve. Methods “COVOID” (COVID-19 Open-Source Infection Dynamics) is a stochastic individual contact model (ICM), which extends the ICMs provided by the open-source EpiModel package for the R statistical computing environment. To demonstrate its use and inform urgent decisions on March 30, 2020, we modeled similar intervention scenarios to those reported by other investigators using various model types, as well as novel scenarios. The scenarios involved isolation of cases, moderate social distancing, and stricter population “lockdowns” enacted over varying time periods in a hypothetical population of 100,000 people. On April 30, 2020, we simulated the epidemic curve for the three contiguous local areas (population 287,344) in eastern Sydney, Australia that recorded 5.3% of Australian cases of COVID-19 through to April 30, 2020, under five different intervention scenarios and compared the modeled predictions with the observed epidemic curve for these areas. Results COVOID allocates each member of a population to one of seven compartments. The number of times individuals in the various compartments interact with each other and their probability of transmitting infection at each interaction can be varied to simulate the effects of interventions. Using COVOID on March 30, 2020, we were able to replicate the epidemic response patterns to specific social distancing intervention scenarios reported by others. The simulated curve for three local areas of Sydney from March 1 to April 30, 2020, was similar to the observed epidemic curve in terms of peak numbers of cases, total numbers of cases, and duration under a scenario representing the public health measures that were actually enacted, including case isolation and ramp-up of testing and social distancing measures. Conclusions COVOID allows rapid modeling of many potential intervention scenarios, can be tailored to diverse settings, and requires only standard computing infrastructure. It replicates the epidemic curves produced by other models that require highly detailed population-level data, and its predicted epidemic curve, using parameters simulating the public health measures that were enacted, was similar in form to that actually observed in Sydney, Australia. Our team and collaborators are currently developing an extended open-source COVOID package comprising of a suite of tools to explore intervention scenarios using several categories of models.

Online first; In France, home confinement is not a common preventive measure against an influenza pandemic, although it is used around the world. Based on a stated method approach, we analyze the attitude that the French would adopt if this measure were put in place. Next, we propose a cost–benefit analysis to discuss the cost-effectiveness of this measure. We find that over three-quarters of respondents report complying with home confinement. Their choice depends on their individual characteristics, the interaction they may have with an infected person and home confinement conditions, but not their experience with preventive measures. We find that behaviors such as sensitivity to certainty, selfishness and altruism emerge. As far as cost-effectiveness is concerned, our study shows that home confinement is a prevention path that should not be neglected and should even be prescribed.; Bien que le confinement soit utilisé dans le monde entier comme mesure préventive contre une pandémie de grippe, en France, il n'est encore pas très employé. Nous analysons l'attitude que les Français adopteraient si cette mesure était mise en place. Ensuite, nous proposons une analyse pour discuter du rapport coût-efficacité de cette mesure. Nous obtenons que plus des trois quarts des répondants déclarent qu'ils se conformeraient au confinement. Leur choix dépend de leurs caractéristiques individuelles, de l'interaction qu'ils peuvent avoir avec une personne infectée et des conditions de confinement, mais pas de leur expérience des mesures préventives. Nous constatons que des comportements tels que la sensibilité à la certitude, l'égoïsme et l'altruisme émergent. En ce qui concerne le rapport coût-efficacité, notre étude montre que le confinement est une voie de prévention qui ne devrait pas être négligée et devrait même être prescrite.

Background: We describe the development and initial achievements of a participatory disease surveillance system that relies on mobile technology to promote Community Level One Health Security (CLOHS) in Africa. Objective: The objective of this system, Enhancing Community-Based Disease Outbreak Detection and Response in East and Southern Africa (DODRES), is to empower community-based human and animal health reporters with training and information and communication technology (ICT)–based solutions to contribute to disease detection and response, thereby complementing strategies to improve the efficiency of infectious disease surveillance at national, regional, and global levels. In this study, we refer to techno-health as the application of ICT-based solutions to enhance early detection, timely reporting, and prompt response to health events in human and animal populations. Methods: An EpiHack, involving human and animal health experts as well as ICT programmers, was held in Tanzania in 2014 to identify major challenges facing early detection, timely reporting, and prompt response to disease events. This was followed by a project inception workshop in 2015, which brought together key stakeholders, including policy makers and community representatives, to refine the objectives and implementation plan of the DODRES project. The digital ICT tools were developed and packaged together as the AfyaData app to support One Health disease surveillance. Community health reporters (CHRs) and officials from animal and human health sectors in Morogoro and Ngorongoro districts in Tanzania were trained to use the AfyaData app. The AfyaData supports near- to real-time data collection and submission at both community and health facility levels as well as the provision of feedback to reporters. The functionality of the One Health Knowledge Repository (OHKR) app has been integrated into the AfyaData app to provide health information on case definitions of diseases of humans and animals and to synthesize advice that can be transmitted to CHRs with next step response activities or interventions. Additionally, a WhatsApp social group was made to serve as a platform to sustain interactions between community members, local government officials, and DODRES team members. Results: Within the first 5 months (August-December 2016) of AfyaData tool deployment, a total of 1915 clinical cases in livestock (1816) and humans (99) were reported in Morogoro (83) and Ngorongoro (1832) districts. Conclusions: These initial results suggest that the DODRES community-level model creates an opportunity for One Health engagement of people in their own communities in the detection of infectious human and animal disease threats. Participatory approaches supported by digital and mobile technologies should be promoted for early disease detection, timely reporting, and prompt response at the community, national, regional, and global levels.

SUMMARYMycoplasma pneumoniaeoutbreaks cause increased use of macrolides and tetracyclines. We aimed to investigate whether drug use data, in addition to laboratory data, could improve understanding of the spread ofM. pneumoniaeepidemics. Number of users ofMycoplasmaantibiotics (erythromycin, doxycycline, clarithromycin) per week and county of residence in an indicator age group (6–12 years) was retrieved from the Norwegian prescription database for the epidemic season 2011–2012 and compared to non-epidemic seasons. In 2011, increased use ofMycoplasmaantibiotics was first observed in September on the west coast of Norway. The Norwegian laboratory-based surveillance system showed the first increase in positive tests in August 2011 and an epidemic was announced on 25 October 2011. At that time the use ofMycoplasmaantibiotics had already exceeded three times the use in non-epidemic periods. Data for three counties from the regional microbiological laboratories showed that the increase in number of positive samples coincided in time with the increase in prescription data. Laboratory data cannot accurately determine the extent of an epidemic, and drug use data cannot identify the cause. Establishing a systematic interaction between the two monitoring systems will enhance surveillance and probably contribute to improved infection control and prudent antibiotic prescribing.

Background: Influenza is a leading cause of death worldwide and contributes to heavy economic losses to individuals and communities. Therefore, the early prediction of and interventions against influenza epidemics are crucial to reduce mortality and morbidity because of this disease. Similar to other countries, the Taiwan Centers for Disease Control and Prevention (TWCDC) has implemented influenza surveillance and reporting systems, which primarily rely on influenza-like illness (ILI) data reported by health care providers, for the early prediction of influenza epidemics. However, these surveillance and reporting systems show at least a 2-week delay in prediction, indicating the need for improvement. Objective: We aimed to integrate the TWCDC ILI data with electronic medical records (EMRs) of multiple hospitals in Taiwan. Our ultimate goal was to develop a national influenza trend prediction and reporting tool more accurate and efficient than the current influenza surveillance and reporting systems. Methods: First, the influenza expertise team at Taipei Medical University Health Care System (TMUHcS) identified surveillance variables relevant to the prediction of influenza epidemics. Second, we developed a framework for integrating the EMRs of multiple hospitals with the ILI data from the TWCDC website to proactively provide results of influenza epidemic monitoring to hospital infection control practitioners. Third, using the TWCDC ILI data as the gold standard for influenza reporting, we calculated Pearson correlation coefficients to measure the strength of the linear relationship between TMUHcS EMRs and regional and national TWCDC ILI data for 2 weekly time series datasets. Finally, we used the Moving Epidemic Method analyses to evaluate each surveillance variable for its predictive power for influenza epidemics. Results: Using this framework, we collected the EMRs and TWCDC ILI data of the past 3 influenza seasons (October 2014 to September 2017). On the basis of the EMRs of multiple hospitals, 3 surveillance variables, TMUHcS-ILI, TMUHcS-rapid influenza laboratory tests with positive results (RITP), and TMUHcS-influenza medication use (IMU), which reflected patients with ILI, those with positive results from rapid influenza diagnostic tests, and those treated with antiviral drugs, respectively, showed strong correlations with the TWCDC regional and national ILI data (r=.86-.98). The 2 surveillance variables—TMUHcS-RITP and TMUHcS-IMU—showed predictive power for influenza epidemics 3 to 4 weeks before the increase noted in the TWCDC ILI reports. Conclusions: Our framework periodically integrated and compared surveillance data from multiple hospitals and the TWCDC website to maintain a certain prediction quality and proactively provide monitored results. Our results can be extended to other infectious diseases, mitigating the time and effort required for data collection and analysis. Furthermore, this approach may be developed as a cost-effective electronic surveillance tool for the early and accurate prediction of epidemics of influenza and other infectious diseases in densely populated regions and nations.