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Background: Race and place intersect to produce location-based variation in disease distributions. We analyzed the geographic distribution of tuberculosis (TB) incidence in Michigan, USA to better understand the complex interplay between race and place, comparing patterns in Detroit, Wayne County and the state of Michigan as a whole.Methods: Using cross-sectional TB surveillance data from the Michigan Department of Health and Human Services, multivariable statistical models were developed to analyze the residence patterns of TB incidence from 2007 through 2012. Two-way interactions among the residence location and race of cases were assessed.Results: Overall, Detroit residents experienced 58% greater TB incidence than residents of Wayne County or the state of Michigan. Racial inequalities were less pronounced in Detroit compared to both Wayne County and the state of Michigan. Blacks in Detroit had 2.01 times greater TB incidence than Whites, while this inequality was 3.62 times more in Wayne County and 8.72 greater in the state of Michigan.Conclusion: Our results highlight how race and place interact to influence patterns of TB disease, and the ways in which this interaction is context dependent. TB elimination in the U.S. will require strategies that address the local social environment, as much as the physical environment. [ABSTRACT FROM AUTHOR]

Background: Human mobility plays a central role in shaping pathogen transmission by generating spatial and/or individual variability in potential pathogen-transmitting contacts. Recent research has shown that symptomatic infection can influence human mobility and pathogen transmission dynamics. Better understanding the complex relationship between symptom severity, infectiousness, and human mobility requires quantification of movement patterns throughout infectiousness. For dengue virus (DENV), human infectiousness peaks 0–2 days after symptom onset, making it paramount to understand human movement patterns from the beginning of illness. Methodology and principal findings: Through community-based febrile surveillance and RT-PCR assays, we identified a cohort of DENV+ residents of the city of Iquitos, Peru (n = 63). Using retrospective interviews, we measured the movements of these individuals when healthy and during each day of symptomatic illness. The most dramatic changes in mobility occurred during the first three days after symptom onset; individuals visited significantly fewer locations (Wilcoxon test, p = 0.017) and spent significantly more time at home (Wilcoxon test, p = 0.005), compared to when healthy. By 7–9 days after symptom onset, mobility measures had returned to healthy levels. Throughout an individual’s symptomatic period, the day of illness and their subjective sense of well-being were the most significant predictors for the number of locations and houses they visited. Conclusions/Significance: Our study is one of the first to collect and analyze human mobility data at a daily scale during symptomatic infection. Accounting for the observed changes in human mobility throughout illness will improve understanding of the impact of disease on DENV transmission dynamics and the interpretation of public health-based surveillance data. [ABSTRACT FROM AUTHOR]

Yellow fever is a vector-borne disease endemic in tropical regions of Africa, where 90% of the global burden occurs, and Latin America. It is notoriously under-reported with uncertainty arising from a complex transmission cycle including a sylvatic reservoir and non-specific symptom set. Resulting estimates of burden, particularly in Africa, are highly uncertain. We examine two established models of yellow fever transmission within a Bayesian model averaging framework in order to assess the relative evidence for each model’s assumptions and to highlight possible data gaps. Our models assume contrasting scenarios of the yellow fever transmission cycle in Africa. The first takes the force of infection in each province to be static across the observation period; this is synonymous with a constant infection pressure from the sylvatic reservoir. The second model assumes the majority of transmission results from the urban cycle; in this case, the force of infection is dynamic and defined through a fixed value of R0 in each province. Both models are coupled to a generalised linear model of yellow fever occurrence which uses environmental covariates to allow us to estimate transmission intensity in areas where data is sparse. We compare these contrasting descriptions of transmission through a Bayesian framework and trans-dimensional Markov chain Monte Carlo sampling in order to assess each model’s evidence given the range of uncertainty in parameter values. The resulting estimates allow us to produce Bayesian model averaged predictions of yellow fever burden across the African endemic region. We find strong support for the static force of infection model which suggests a higher proportion of yellow fever transmission occurs as a result of infection from an external source such as the sylvatic reservoir. However, the model comparison highlights key data gaps in serological surveys across the African endemic region. As such, conclusions concerning the most prevalent transmission routes for yellow fever will be limited by the sparsity of data which is particularly evident in the areas with highest predicted transmission intensity. Our model and estimation approach provides a robust framework for model comparison and predicting yellow fever burden in Africa. However, key data gaps increase uncertainty surrounding estimates of model parameters and evidence. As more mathematical models are developed to address new research questions, it is increasingly important to compare them with established modelling approaches to highlight uncertainty in structures and data. [ABSTRACT FROM AUTHOR]

Background: During infectious disease outbreaks with pandemic potential, the number of air passengers travelling from the outbreak source to international destinations has been used as a proxy for disease importation risk to new locations. However, evaluations of the validity of this approach are limited. We sought to quantify the association between international air travel and disease importation using the 2014-2016 chikungunya outbreak in the Americas as a case study.Methods: We used country-level chikungunya case data to define a time period of epidemic activity for each of the 45 countries and territories in the Americas reporting outbreaks between 2014 and 2016. For each country, we identified airports within or proximate to areas considered suitable for chikungunya transmission and summed the number of commercial air passengers departing from these airports during the epidemic period to each US state. We used negative binomial models to quantify the association between the number of incoming air passengers from countries experiencing chikungunya epidemics and the annual rate of chikungunya importation into the USA at the state level.Results: We found a statistically significant positive association between passenger flows via airline travel from countries experiencing chikungunya epidemics and the number of imported cases in the USA at the state level (P < 0.0001). Additionally, we found that as the number of arriving airline passengers increased by 10%, the estimated number of imported cases increased by 5.2% (95% CI: 3.0-7.6).Conclusion: This validation study demonstrated that air travel was strongly associated with observed importation of chikungunya cases in the USA and can be a useful proxy for identifying areas at increased risk for disease importation. This approach may be useful for understanding exportation risk of other arboviruses. [ABSTRACT FROM AUTHOR]

The dynamics of infectious diseases are greatly influenced by the movement of both susceptible and infected hosts. To accurately represent disease dynamics among a mobile host population, detailed movement models have been coupled with disease transmission models. However, a number of different host movement models have been proposed, each with their own set of assumptions and results that differ from the other models. Here, we compare two movement models coupled to the same disease transmission model using network analyses. This application of network analysis allows us to evaluate the fit and accuracy of the movement model in a multilevel modeling framework with more detail than established statistical modeling fitting methods. We used data that detailed mobile pastoralists’ movements as input for 100 stochastic simulations of a Spatio-Temporal Movement (STM) model and 100 stochastic simulations of an Individual Movement Model (IMM). Both models represent dynamic movement and subsequent contacts. We generated networks in which nodes represent camps and edges represent the distance between camps. We simulated pathogen transmission over these networks and tested five network metrics–strength, betweenness centrality, three-step reach, density, and transitivity–to determine which could predict disease simulation outcomes and thereby be used to correlate model simulation results with disease transmission simulations. We found that strength, network density, and three-step reach of movement model results correlated with the final epidemic size of outbreak simulations. Betweenness centrality only weakly correlated for the IMM model. Transitivity only weakly correlated for the STM model and time-varying IMM model metrics. We conclude that movement models coupled with disease transmission models can affect disease transmission results and should be carefully considered and vetted when modeling pathogen spread in mobile host populations. Strength, network density, and three-step reach can be used to evaluate movement models before disease simulations to predict final outbreak sizes. These findings can contribute to the analysis of multilevel models across systems. [ABSTRACT FROM AUTHOR]

For the evaluation of infectious-diseases interventions, the transmissible nature of such diseases plays a central role. Agent-based models (ABM) allow for dynamic transmission modeling but publications are limited. We aim to provide an overview of important characteristics of ABM for decision-analytic modeling of infectious diseases. A case study of dengue epidemics illustrates model characteristics, conceptualization, calibration and model analysis. First, major characteristics of ABM are outlined and discussed based on ISPOR and ISPOR-SMDM Good Practice guidelines. Second, in our case study, we modeled a dengue outbreak in Cebu City (Philippines) to assess the impact interventions to control the relative growth of the mosquito population. Model outcomes include prevalence and incidence of infected persons. The modular ABM simulates persons and mosquitoes over an annual time horizon considering daily time steps. The model was calibrated and validated. ABM is a dynamic, individual-level modeling approach that is capable to reproduce direct and indirect effects of interventions for infectious diseases. The ability to replicate emerging behavior and to include human behavior or the behavior of other agents is a distinguishing modeling characteristic (e.g., compared to Markov models). Modeling behavior may, however, require extensive calibration and validation. The analyzed hypothetical effectiveness of dengue interventions showed that a reduced human-mosquito ratio of 1:2.5 during rainy seasons leads already to a substantial decrease of infected persons. ABM can support decision-analyses for infectious diseases including disease dynamics, emerging behavior, and providing a high level of reusability due to modularity. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance is one of the major public health threats of the 21st century. There is a pressing need to adopt more efficient treatment strategies in order to prevent the emergence and spread of resistant strains. The common approach is to treat patients with high drug doses, both to clear the infection quickly and to reduce the risk of de novo resistance. Recently, several studies have argued that, at least in some cases, low-dose treatments could be more suitable to reduce the within-host emergence of antimicrobial resistance. However, the choice of a drug dose may have consequences at the population level, which has received little attention so far. Here, we study the influence of the drug dose on resistance and disease management at the host and population levels. We develop a nested two-strain model and unravel trade-offs in treatment benefits between an individual and the community. We use several measures to evaluate the benefits of any dose choice. Two measures focus on the emergence of resistance, at the host level and at the population level. The other two focus on the overall treatment success: the outbreak probability and the disease burden. We find that different measures can suggest different dosing strategies. In particular, we identify situations where low doses minimize the risk of emergence of resistance at the individual level, while high or intermediate doses prove most beneficial to improve the treatment efficiency or even to reduce the risk of resistance in the population. [ABSTRACT FROM AUTHOR]

Background: The number of dengue fever (DF) cases and the number of dengue outbreaks have increased in recent years in Zhejiang Province, China. An unexpected dengue outbreak was reported in Hangzhou in 2017 and caused more than one thousand dengue cases. This study was undertaken to evaluate the effectiveness of the actual control measures, estimate the proportion of inapparent infections during this outbreak and simulate epidemic development based on different levels of control measures taking inapparent infections into consideration. Methods: The epidemic data of dengue cases in Hangzhou, Zhejiang Province, in 2017 and the number of the people exposed to the outbreaks were obtained from the China Information Network System of Disease Prevention and Control. The epidemic without intervention measures was used to estimate the unknown parameters. A susceptible-exposed-infectious/inapparent-recovered (SEIAR) model was used to estimate the effectiveness of the control interventions. The inapparent infections were also evaluated at the same time. Results: In total, 1137 indigenous dengue cases were reported in Hangzhou in 2017. The number of indigenous dengue cases was estimated by the SEIAR model. This number was predicted to reach 6090 by the end of November 2, 2017, if no control measures were implemented. The total number of reported cases was reduced by 81.33% in contrast to the estimated incidence without intervention. The number of average daily inapparent cases was 10.18 times higher than the number of symptomatic cases. The earlier and more rigorously the vector control interventions were implemented, the more effective they were. The results showed that implementing vector control continuously for more than twenty days was more effective than every few days of implementation. Case isolation is not sufficient enough for epidemic control and only reduced the incidence by 38.10% in contrast to the estimated incidence without intervention, even if case isolation began seven days after the onset of the first case. Conclusions: The practical control interventions in the outbreaks that occurred in Hangzhou City were highly effective. The proportion of inapparent infections was large, and it played an important role in transmitting the disease during this epidemic. Early, continuous and high efficacy vector control interventions are necessary to limit the development of a dengue epidemic. Timely diagnosis and case reporting are important in the intervention at an early stage of the epidemic. [ABSTRACT FROM AUTHOR]

We present the first analysis of face-to-face contact network data from Niakhar, Senegal. Participants in a cluster-randomized influenza vaccine trial were interviewed about their contact patterns when they reported symptoms during their weekly household surveillance visit. We employ a negative binomial model to estimate effects of covariates on contact degree. We estimate the mean contact degree for asymptomatic Niakhar residents to be 16.5 (95% C.I. 14.3, 18.7) in the morning and 14.8 in the afternoon (95% C.I. 12.7, 16.9). We estimate that symptomatic people make 10% fewer contacts than asymptomatic people (95% C.I. 5%, 16%; p = 0.006), and those aged 0-5 make 33% fewer contacts than adults (95% C.I. 29%, 37%; p < 0.001). By explicitly modelling the partial rounding pattern observed in our data, we make inference for both the underlying (true) distribution of contacts as well as for the reported distribution. We created an estimator for homophily by compound (household) membership and estimate that 48% of contacts by symptomatic people are made to their own compound members in the morning (95% CI, 45%, 52%) and 60% in the afternoon/evening (95% CI, 56%, 64%). We did not find a significant effect of symptom status on compound homophily. We compare our findings to those from other countries and make design recommendations for future surveys. [ABSTRACT FROM AUTHOR]

Contact patterns strongly influence the dynamics of disease transmission in both human and non-human animal populations. Domestic dogs Canis familiaris are a social species and are a reservoir for several zoonotic infections, yet few studies have empirically determined contact patterns within dog populations. Using high-resolution proximity logging technology, we characterised the contact networks of free-ranging domestic dogs from two settlements (n = 108 dogs, covering >80% of the population in each settlement) in rural Chad. We used these data to simulate the transmission of an infection comparable to rabies and investigated the effects of including observed contact heterogeneities on epidemic outcomes. We found that dog contact networks displayed considerable heterogeneity, particularly in the duration of contacts and that the network had communities that were highly correlated with household membership. Simulations using observed contact networks had smaller epidemic sizes than those that assumed random mixing, demonstrating the unsuitability of homogenous mixing models in predicting epidemic outcomes. When contact heterogeneities were included in simulations, the network position of the individual initially infected had an important effect on epidemic outcomes. The risk of an epidemic occurring was best predicted by the initially infected individual’s ranked degree, while epidemic size was best predicted by the individual’s ranked eigenvector centrality. For dogs in one settlement, we found that ranked eigenvector centrality was correlated with range size. Our results demonstrate that observed heterogeneities in contacts are important for the prediction of epidemiological outcomes in free-ranging domestic dogs. We show that individuals presenting a higher risk for disease transmission can be identified by their network position and provide evidence that observable traits hold potential for informing targeted disease management strategies. [ABSTRACT FROM AUTHOR]

Background: Leptospirosis is one of the most important neglected tropical bacterial diseases worldwide. However, there is limited information on the genetic diversity and host selectivity of pathogenic Leptospira in wild small mammal populations. Methodology/Principal findings: Jiangxi Province, located in southern China, is a region highly endemic for leptospirosis. In this study, among a total of 3,531 trapped rodents dominated by Apodemus agrarius (59.7%), 330 Leptospira strains were successfully isolated from six different sites in Jiangxi between 2002 and 2015. Adding 71 local strains from humans, various kinds of livestock and wild animals in Jiangxi, a total of 401 epidemic strains were characterized using 16S rRNA gene senquencing, multilocus sequence typing (MLST) and the microscopic agglutination test (MAT). Among them, the most prevalent serogroup was Icterohaemorrhagiae (61.10%), followed by Javanica (19.20%) and Australis (9.73%); the remaining five serogroups, Canicola, Autumnalis, Grippotyphosa, Hebdomadis and Pomona, accounted for 9.97%. Species identification revealed that 325 were L. interrogans and 76 were L. borgpetersenii. Moreover, L. interrogans was the only pathogenic species in Fuliang and Shanggao and was predominant in Shangrao (95.0%); L. borgpetersenii was the most common in the remaining three sites. Twenty-one sequence types (STs) were identified. Similarly, ST1 and serogroup Icterohaemorrhagiae were most prevalent in Shangrao (86.0% and 86.4%) and Fuliang (90.4% and 90.4%), ST143 and serogroup Javanica in Shangyou (88.5% and 90.4%) and Longnan (73.1% and 73.1%), and ST105 and serogroup Australis in Shanggao (46.3% and 56.1%). Serogroup Icterohaemorhagiae primarily linked to A. agrarius (86.9%), serogroup Canicola to dogs (83.3%). There were significant differences in the distribution of leptospiral species/serogroups/STs prevalence across host species/collected locations among the 394 animal-associated strains (Fisher’s exact test, p<0.001). Conclusions/Significance: Our study demonstrated high genetic diversity of pathogenic Leptospira strains from wild small animals in Jiangxi from 2002 to 2015. A. agrarius was the most abundantly trapped animal reservoir, and serogroup Icterohaemorrhagiae and ST1 were the most dominant in Jiangxi. Significant geographic variation and host diversity in the distribution of dominant species, STs and serogroups were observed. Moreover, rat-to-human transmission might play a crucial role in the circulation of Leptospirosis in Jiangxi. Details of the serological and molecular characteristics circulating in this region will be essential in implementing prevention and intervention measures to reduce the risk of disease transmission in China. However, phylogenetic analysis of more Leptospira isolates should explore the impact of ecological change on leptospirosis transmission dynamics and investigate how such new knowledge might better impact environmental monitoring for disease control and prevention at a public health level. [ABSTRACT FROM AUTHOR]

The objective of this study was to conduct a systematic analysis of the reporting quality of the Ebola Virus Disease (EVD) outbreak in West Africa from 2014–2018 using the Modified STROBE statement. We included studies on the 2014 EVD outbreak alone, limited to those on human patients in Africa. We searched the following databases (MEDLINE, EMBASE, and Web of Science) for outbreak reports published between 2014–2018. We assessed factors potentially associated with the quality of reporting. A total of 69 of 131 (53%) articles within the full-text review fulfilled our eligibility criteria and underwent the Modified STROBE assessment for analyzing the quality of reporting. The Modified STROBE scores of the included studies ranged from 11–26 points and the mean was found to be 19.54 out of 30 with a standard deviation (SD) of ± 4.30. The top three reported Modified STROBE components were descriptive characteristics of study participants, scientific background and evidence rational, and clinical significance of observations. More than 75% of the studies met a majority of the criteria in the Modified STROBE assessment tool. Information that was commonly missing included addressing potential source of bias, sensitivity analysis, further results/analysis such as risk estimates and odds ratios, presence of a flowchart, and addressing missing data. In multivariable analysis, peer-reviewed publication was the only predictor that remained significantly associated with a higher Modified STROBE score. In conclusion, the large range of Modified STROBE scores observed indicates variability in the quality of outbreak reports for EVD. The review identified strong reporting in some areas, whereas other areas are in need of improvement, in particular providing an important description of the outbreak setting and identifying any external elements (potential biases and confounding factors) that could hinder the credibility of the findings. [ABSTRACT FROM AUTHOR]

As emerging and re-emerging infectious arboviruses like dengue, chikungunya, and Zika threaten new populations worldwide, officials scramble to assess local severity and transmissibility, with little to no epidemiological history to draw upon. Indirect estimates of risk from vector habitat suitability maps are prone to great uncertainty, while direct estimates from epidemiological data are only possible after cases accumulate and, given environmental constraints on arbovirus transmission, cannot be widely generalized beyond the focal region. Combining these complementary methods, we use disease importation and transmission data to improve the accuracy and precision of a priori ecological risk estimates. We demonstrate this approach by estimating the spatiotemporal risks of Zika virus transmission throughout Texas, a high-risk region in the southern United States. Our estimates are, on average, 80% lower than published ecological estimates—with only six of 254 Texas counties deemed capable of sustaining a Zika epidemic—and they are consistent with the number of autochthonous cases detected in 2017. Importantly our method provides a framework for model comparison, as our mechanistic understanding of arbovirus transmission continues to improve. Real-time updating of prior risk estimates as importations and outbreaks arise can thereby provide critical, early insight into local transmission risks as emerging arboviruses expand their global reach. [ABSTRACT FROM AUTHOR]

The preparedness of health systems to detect, treat, and prevent onward transmission of Ebola virus disease (EVD) is central to mitigating future outbreaks. Early detection of outbreaks is critical to timely response, but estimating detection rates is difficult because unreported spillover events and outbreaks do not generate data. Using three independent datasets available on the distributions of secondary infections during EVD outbreaks across West Africa, in a single district (Western Area) of Sierra Leone, and in the city of Conakry, Guinea, we simulated realistic outbreak size distributions and compared them to reported outbreak sizes. These three empirical distributions lead to estimates for the proportion of detected spillover events and small outbreaks of 26% (range 8–40%, based on the full outbreak data), 48% (range 39–62%, based on the Sierra Leone data), and 17% (range 11–24%, based on the Guinea data). We conclude that at least half of all spillover events have failed to be reported since EVD was first recognized. We also estimate the probability of detecting outbreaks of different sizes, which is likely less than 10% for single-case spillover events. Comparing models of the observation process also suggests the probability of detecting an outbreak is not simply the cumulative probability of independently detecting any one individual. Rather, we find that any individual’s probability of detection is highly dependent upon the size of the cluster of cases. These findings highlight the importance of primary health care and local case management to detect and contain undetected early stage outbreaks at source. [ABSTRACT FROM AUTHOR]

Planning for a re-emergent epidemic of smallpox requires surge capacity of space, resources and personnel within health systems. There are many uncertainties in such a scenario, including likelihood and size of an attack, speed of response and health system capacity. We used a model for smallpox transmission to determine requirements for hospital beds, contact tracing and health workers (HCWs) in Sydney, Australia, during a modelled epidemic of smallpox. Sensitivity analysis was done on attack size, speed of response and proportion of case isolation and contact tracing. We estimated 100638 clinical HCWs and 14595 public hospital beds in Sydney. Rapid response, case isolation and contact tracing are influential on epidemic size, with case isolation more influential than contact tracing. With 95% of cases isolated, outbreak control can be achieved within 100 days even with only 50% of contacts traced. However, if case isolation and contact tracing both fall to 50%, epidemic control is lost. With a smaller initial attack and a response commencing 20 days after the attack, health system impacts are modest. The requirement for hospital beds will vary from up to 4% to 100% of all available beds in best and worst case scenarios. If the response is delayed, or if the attack infects 10000 people, all available beds will be exceeded within 40 days, with corresponding surge requirements for clinical health care workers (HCWs). We estimated there are 330 public health workers in Sydney with up to 940,350 contacts to be traced. At least 3 million respirators will be needed for the first 100 days. To ensure adequate health system capacity, rapid response, high rates of case isolation, excellent contact tracing and vaccination, and protection of HCWs should be a priority. Surge capacity must be planned. Failures in any of these could cause health system failure, with inadequate beds, quarantine spaces, personnel, PPE and inability to manage other acute health conditions. [ABSTRACT FROM AUTHOR]

Estimating the tick-borne encephalitis (TBE) infection risk under substantial uncertainties of the vector abundance, environmental condition and human-tick interaction is important for evidence-informed public health intervention strategies. Estimating this risk is computationally challenging since the data we observe, i.e., the human incidence of TBE, is only the final outcome of the tick-host transmission and tick-human contact processes. The challenge also increases since the complex TBE virus (TBEV) transmission cycle involves the non-systemic route of transmission between co-feeding ticks. Here, we describe the hidden Markov transition process, using a novel TBEV transmission-human case reporting cascade model that couples the susceptible-infected compartmental model describing the TBEV transmission dynamics among ticks, animal hosts and humans, with the stochastic observation process of human TBE reporting given infection. By fitting human incidence data in Hungary to the transmission model, we estimate key parameters relevant to the tick-host interaction and tick-human transmission. We then use the parametrized cascade model to assess the transmission potential of TBEV in the enzootic cycle with respect to the climate change, and to evaluate the contribution of non-systemic transmission. We show that the TBEV transmission potential in the enzootic cycle has been increasing along with the increased temperature though the TBE human incidence has dropped since 1990s, emphasizing the importance of persistent public health interventions. By demonstrating that non-systemic transmission pathway is a significant factor in the transmission of TBEV in Hungary, we conclude that the risk of TBE infection will be highly underestimated if the non-systemic transmission route is neglected in the risk assessment. [ABSTRACT FROM AUTHOR]

Introduction and aims: Since 2013 MenC and MenW disease incidence and associated mortality rates have increased in the Republic of Ireland. From 2002/2003 to 2012/2013, the average annual MenC incidence was 0.08/100,000, which increased to 0.34/100,000 during 2013/2014 to 2017/18, peaking in 2016/17 (0.72/100,000) with an associated case fatality rate (CFR) of 14.7%. MenW disease incidence has increased each year from 0.02/100,000 in 2013/2014, to 0.29/100,000 in 2017/18, with an associated CFR of 28.6%. We aimed to characterise and relate recent MenC isolates to the previously prevalent MenC:cc11 ET-15 clones, and also characterise and relate recent MenW isolates to the novel ‘Hajj’ clones. Methods: Using WGS we characterised invasive (n = 74, 1997/98 to 2016/17) and carried (n = 16, 2016/17) MenC isolates, and invasive (n = 18, 2010/11 to 2016/17) and carried (n = 15, 2016/17) MenW isolates. Genomes were assembled using VelvethOptimiser and stored on the PubMLST Neisseria Bacterial Isolate Genome Sequence Database. Isolates were compared using the cgMLST approach. Results: Most MenC and MenW isolates identified were cc11. A single MenC:cc11 sub-lineage contained the majority (68%, n = 19/28) of recent MenC:cc11 disease isolates and all carried MenC:cc11 isolates, which were interspersed and distinct from the historically significant ET-15 clones. MenW:cc11 study isolates clustered among international examples of both the original UK 2009 MenW:cc11, and novel 2013 MenW:cc11clones. Conclusions: We have shown that the majority of recent MenC disease incidence was caused by strain types distinct from the MenC:cc11 ET-15 clone of the late 1990s, which still circulate but have caused only sporadic disease in recent years. We have identified that the same aggressive MenW clone now established in several other European countries, is endemic in the RoI and responsible for the recent MenW incidence increases. This data informed the National immunisation Advisory Committee, who are currently deliberating a vaccine policy change to protect teenagers. [ABSTRACT FROM AUTHOR]

The effectiveness of a mass vaccination program can engender its own undoing if individuals choose to not get vaccinated believing that they are already protected by herd immunity. This would appear to be the optimal decision for an individual, based on a strategic appraisal of her costs and benefits, even though she would be vulnerable during subsequent outbreaks if the majority of the population argues in this manner. We investigate how voluntary vaccination can nevertheless emerge in a social network of rational agents, who make informed decisions whether to be vaccinated, integrated with a model of epidemic dynamics. The information available to each agent includes the prevalence of the disease in their local network neighborhood and/or globally in the population, as well as the fraction of their neighbors that are protected against the disease. Crucially, the payoffs governing the decision of agents vary with disease prevalence, resulting in the vaccine uptake behavior changing in response to contagion spreading. The collective behavior of the agents responding to local prevalence can lead to a significant reduction in the final epidemic size, particularly for less contagious diseases having low basic reproduction number . Near the epidemic threshold () the use of local prevalence information can result in divergent responses in the final vaccine coverage. Our results suggest that heterogeneity in the risk perception resulting from the spatio-temporal evolution of an epidemic differentially affects agents’ payoffs, which is a critical determinant of the success of voluntary vaccination schemes. [ABSTRACT FROM AUTHOR]

Simulation studies are often used to predict the expected impact of control measures in infectious disease outbreaks. Typically, two independent sets of simulations are conducted, one with the intervention, and one without, and epidemic sizes (or some related metric) are compared to estimate the effect of the intervention. Since it is possible that controlled epidemics are larger than uncontrolled ones if there is substantial stochastic variation between epidemics, uncertainty intervals from this approach can include a negative effect even for an effective intervention. To more precisely estimate the number of cases an intervention will prevent within a single epidemic, here we develop a 'single-world' approach to matching simulations of controlled epidemics to their exact uncontrolled counterfactual. Our method borrows concepts from percolation approaches, prunes out possible epidemic histories and creates potential epidemic graphs (i.e. a mathematical representation of all consistent epidemics) that can be 'realized' to create perfectly matched controlled and uncontrolled epidemics. We present an implementation of this method for a common class of compartmental models (e.g. SIR models), and its application in a simple SIR model. Results illustrate how, at the cost of some computation time, this method substantially narrows confidence intervals and avoids nonsensical inferences. [ABSTRACT FROM AUTHOR]

Emerging and re-emerging pathogens exhibit very complex dynamics, are hard to model and difficult to predict. Their dynamics might appear intractable. However, new statistical approaches—rooted in dynamical systems and the theory of stochastic processes—have yielded insight into the dynamics of emerging and re-emerging pathogens. We argue that these approaches may lead to new methods for predicting epidemics. This perspective views pathogen emergence and re-emergence as a “critical transition,” and uses the concept of noisy dynamic bifurcation to understand the relationship between the system observables and the distance to this transition. Because the system dynamics exhibit characteristic fluctuations in response to perturbations for a system in the vicinity of a critical point, we propose this information may be harnessed to develop early warning signals. Specifically, the motion of perturbations slows as the system approaches the transition. [ABSTRACT FROM AUTHOR]

Abstract West Nile virus (WNV) transmission risk is strongly related to weather conditions due to the sensitivity of the mosquitoes to climatic factors. We assess the WNV transmission risk of humans to seasonal weather conditions and the relative effects of parameters affecting the transmission dynamics. The assessment involves a known epidemiological model we extend to account for temperature and precipitation and a global uncertainty and sensitivity analysis framework. We focus on three relevant quantities, the basic reproduction number (R 0), the minimum infection rate (MIR), and the number of infected individuals. The highest-priority weather-related WNV transmission risks can be attributed to the birth and death rate of mosquitoes, the biting rate of mosquitoes to birds, and the probability of transmission from birds to mosquitoes. Global sensitivity analysis indicates that these parameters make up a big part of the explained variance in R 0 and MIR. The analysis allows for a dynamic assessment over time capturing the period parameters are more relevant than others. Global uncertainty and sensitivity analysis of WNV transmission risk to humans enable insights into the relative importance of individual parameters of the transmission cycle of the virus facilitating the understanding of the dynamics and the implementation of tailored control strategies. [ABSTRACT FROM AUTHOR]

Background: Numerous urban villages (UVs) and frequent infectious disease outbreaks are major environmental and public health concerns in highly urbanized regions, especially in developing countries. However, the spatial and quantitative associations between UVs and infections remain little understood on a fine scale. Methodology and principal findings: In this study, the relationships between reported dengue fever (DF) epidemics during 2012–2017, gross domestic product (GDP), the traffic system (road density, bus and/or subway stations), and UVs derived from high-resolution remotely sensed imagery in the central area of Guangzhou, were explored using geographically weighted regression (GWR) models based on a 1 km × 1 km grid scale. Accounting for 16.53%–18.07% of residential area and 16.84%–18.02% of population, UVs possessed 28.55%–38.24% of total reported DF cases in the core area of Guangzhou. The density of DF cases and the DF incidence rates in UVs were 1.81–3.13 and 1.82–3.06 times of that of normal construction land. Approximately 90% of the total cases were concentrated in the UVs and their buffering zones of radius ranged from 0 to 500 m. Significantly positive associations were observed between gridded DF incidence rates and UV area (r = 0.33, P = 0.000), the number of bus stops (r = 0.49, P = 0.000) and subway stations (r = 0.27, P = 0.000), and road density (r = 0.39, P = 0.000). About 60% of spatial variations in the gridded DF incidence rates were interpreted by the different variables of GDP, UVs, and bus stops integrated in GWR models. Conclusions: UVs likely acted as special transfer stations, receiving and/or exporting DF cases during epidemics. This work increases our understanding of the influences of UVs on vector-borne diseases in highly urbanized areas, supplying valuable clues to local authorities making targeted interventions for the prevention and control of DF epidemics. [ABSTRACT FROM AUTHOR]

Background: The cestode Taenia solium causes the neglected (zoonotic) tropical disease cysticercosis, a leading cause of preventable epilepsy in endemic low and middle-income countries. Transmission models can inform current scaling-up of control efforts by helping to identify, validate and optimise control and elimination strategies as proposed by the World Health Organization (WHO). Methodology/Principal findings: A systematic literature search was conducted using the PRISMA approach to identify and compare existing T. solium transmission models, and related Taeniidae infection transmission models. In total, 28 modelling papers were identified, of which four modelled T. solium exclusively. Different modelling approaches for T. solium included deterministic, Reed-Frost, individual-based, decision-tree, and conceptual frameworks. Simulated interventions across models agreed on the importance of coverage for impactful effectiveness to be achieved. Other Taeniidae infection transmission models comprised force-of-infection (FoI), population-based (mainly Echinococcus granulosus) and individual-based (mainly E. multilocularis) modelling approaches. Spatial structure has also been incorporated (E. multilocularis and Taenia ovis) in recognition of spatial aggregation of parasite eggs in the environment and movement of wild animal host populations. Conclusions/Significance: Gaps identified from examining the wider Taeniidae family models highlighted the potential role of FoI modelling to inform model parameterisation, as well as the need for spatial modelling and suitable structuring of interventions as key areas for future T. solium model development. We conclude that working with field partners to address data gaps and conducting cross-model validation with baseline and longitudinal data will be critical to building consensus-led and epidemiological setting-appropriate intervention strategies to help fulfil the WHO targets. [ABSTRACT FROM AUTHOR]

Background: Leprosy is a slow, chronic disorder caused by Mycobacterium leprae. India has achieved elimination of leprosy in December 2005 but new cases are being detected and continue to occur in some endemic pockets. The possible ways of transmission of leprosy is not fully understood and is believed that leprosy is transmitted from person to person in long term contact. Studying the transmission dynamics is further complicated by inability to grow M. leprae in culture medium and lack of animal models. More than one family members were found to be affected by leprosy in some highly endemic pockets. This study reported the transmission pattern of leprosy in a family having 4 patients. Methodology/Principal findings: We investigated the transmission of leprosy in a single family having 4 patients using microsatellite typing. DNA was isolated from slit skin smear samples taken from the patients and the isolated DNA were amplified using microsatellite loci TA11CA3. The amplified products were sequenced using Sanger’s sequencing methods and the copy number variation in the microsatellite loci between strains were elucidated by multiple sequence alignment. The result showed that all the 4 members of the family acquired infection from 3 different strains of M. leprae from 3 different sources. The elder and middle daughters were infected by same types of strains having the repeat unit TA13CA3 and could have acquired the infection from social contacts of leprosy cases while the father and younger daughter were infected by strains with the repeat unit TA12CA3 and TA11CA3 and could have acquired infection from social contacts. Conclusions/Significance: The study suggested that three family members viz, elder daughter, father and younger daughter could be infected by M. leprae from 3 different sources and the history of the disease and genetic analysis showed that the middle daughter acquired infection from her elder sister in due course of contact. This study implies that the transmission of leprosy not only occurred amongst the house hold members but also has been transmitted from social and neighborhood contacts in long term association with the them. [ABSTRACT FROM AUTHOR]

Mass media reports can induce individual behaviour change during a disease outbreak, which has been found to be useful as it reduces the force of infection. We propose a compartmental model by including a new compartment of the intensity of the media reports, which extends existing models by considering a novel media function, which is dependent both on the number of infected individuals and on the intensity of mass media. The existence and stability of the equilibria are analyzed and an optimal control problem of minimizing the total number of cases and total cost is considered, using reduction or enhancement in the media reporting rate as the control. With the help of Pontryagin's Maximum Principle, we obtain the optimal media reporting intensity. Through parameterization of the model with the 2009 A/H1N1 influenza outbreak data in the 8th Hospital of Xi'an in Shaanxi Province of China, we obtain the basic reproduction number for the formulated model with two particular media functions. The optimal media reporting intensity obtained here indicates that during the early stage of an epidemic we should quickly enhance media reporting intensity, and keep it at a maximum level until it can finally weaken when epidemic cases have decreased significantly. Numerical simulations show that media impact reduces the number of cases during an epidemic, but that the number of cases is further mitigated under the optimal reporting intensity. Sensitivity analysis implies that the outbreak severity is more sensitive to the weight α1 (weight of media effect sensitive to infected individuals) than weight α2 (weight of media effect sensitive to media items). [ABSTRACT FROM AUTHOR]

Abstract Background Pertussis is a re-emerging disease worldwide despite its high vaccination coverage. European and Latin-American countries have used different surveillance and vaccination policies against pertussis. We compared the epidemiology of this disease in two Ibero-American countries with different vaccination and surveillance policies. Methods We compared the epidemiology of pertussis in Spain and the Dominican Republic (DR). We present a 10-year observational study of reported pertussis based on suspected and/or probable cases of pertussis identified by the national mandatory reporting system in both countries between 2005 and 2014. Both countries have a similar case definition for pertussis surveillance, although Spain applies laboratory testing, and uses real time PCR and/or culture for case confirmation while in DR only probable and/or suspected cases are reported. We analyzed incidence, hospitalization, case-fatality rates, mortality and vaccination coverage. Results The average annual incidence in children aged

Background: The increasing prevalence of penicillin non-susceptible pneumococci (PNSP) throughout the world threatens successful treatment of infections caused by this important bacterial pathogen. The rate at which PNSP clones spread in the community is thought to mainly be determined by two key determinants; the volume of penicillin use and the magnitude of the fitness cost in the absence of treatment. The aim of the study was to determine the impacts of penicillin consumption and fitness cost on pneumococcal transmission dynamics in a developed country setting. Methods: An individual-based network model based on real-life demographic data was constructed and applied in a developed country setting (Sweden). A population structure with transmission of carriage taking place within relevant mixing groups, i.e. families, day care groups, school classes, and other close contacts, was considered to properly assess the transmission dynamics for susceptible and PNSP clones. Several scenarios were simulated and model outcomes were statistically analysed. Results: Model simulations predicted that with an outpatient penicillin use corresponding to the sales in Sweden 2010 (118 recipes per 1,000 inhabitants per year), the magnitude of a fitness cost for resistance must be at least 5% to offset the advantage of penicillin resistance. Moreover, even if there is a fitness cost associated with penicillin resistance, a considerable reduction of penicillin usage appears to be required to significantly decrease the incidence of PNSP in a community. Conclusion: The frequency of PNSP clones is hard to reverse by simply reducing the penicillin consumption even if there is a biological cost associated with resistance. However, because penicillin usage does promote further spread of PNSP clones, it is important to keep down penicillin consumption considering future resistance problems.

To estimate and predict the transmission dynamics of respiratory viruses, the estimation of the basic reproduction number, R0, is essential. Recently, approximate Bayesian computation methods have been used as likelihood free methods to estimate epidemiological model parameters, particularly R0. In this paper, we explore various machine learning approaches, the multi-layer perceptron, convolutional neural network, and long-short term memory, to learn and estimate the parameters. Further, we compare the accuracy of the estimates and time requirements for machine learning and the approximate Bayesian computation methods on both simulated and real-world epidemiological data from outbreaks of influenza A(H1N1)pdm09, mumps, and measles. We find that the machine learning approaches can be verified and tested faster than the approximate Bayesian computation method, but that the approximate Bayesian computation method is more robust across different datasets.

Malaria is endemic in Guinea; however, the extent and role in transmission of asymptomatic malaria are not well understood. In May 2023, we conducted a rapid community survey to determine Plasmodium falciparum ( P. falciparum ) prevalence among asymptomatic individuals in Middle Guinea (Prefecture Dalaba) and Forest Guinea (Prefecture Guéckédou). In Dalaba, 6 of 239 (2.1%, confidence interval (CI) 0.9-4.8%) individuals tested positive for P. falciparum by a rapid diagnostic test (RDT), while in Guéckédou, 147 of 235 (60.9%, CI 54.5-66.9%) participants tested positive. Asymptomatic malaria needs to be considered more strongly as a driver of transmission when designing control strategies, especially in Forest Guinea and potentially other hyper-endemic settings.

A key challenge for public health policymakers is determining when an infectious disease outbreak has finished. Following a period without cases, an estimate of the probability that no further cases will occur in future (the end-of-outbreak probability) can be used to inform whether or not to declare an outbreak over. An existing quantitative approach (the Nishiura method), based on a branching process transmission model, allows the end-of-outbreak probability to be approximated from disease incidence time series, the offspring distribution and the serial interval distribution. Here, we show how the end-of-outbreak probability under the same transmission model can be calculated exactly if data describing who-infected-whom (the transmission tree) are also available (e.g. from contact tracing studies). In that scenario, our novel approach (the traced transmission method) is straightforward to use. We demonstrate this by applying the method to data from previous outbreaks of Ebola virus disease and Nipah virus infection. For both outbreaks, the traced transmission method would have determined that the outbreak was over earlier than the Nishiura method. This highlights that collection of contact tracing data and application of the traced transmission method may allow stringent control interventions to be relaxed quickly at the end of an outbreak, with only a limited risk of outbreak resurgence.

Background: Neoadjuvant therapy has emerged as an alternative treatment strategy for potentially resectable pancreatic cancer. In the absence of large randomized controlled trials offering a direct comparison, this study aims to use Markov decision analysis to compare efficacy of traditional surgery first (SF) and neoadjuvant treatment (NAT) pathways for potentially resectable pancreatic cancer. Methods: An advanced Markov decision analysis model was constructed to compare SF and NAT pathways for potentially resectable pancreatic cancer. Transition probabilities were calculated from randomized control and Phase II/III trials after comprehensive literature search. Utility outcomes were measured in overall and quality-adjusted life months (QALMs) on an intention-to-treat basis as the primary outcome. Markov cohort analysis of treatment received was the secondary outcome. Model uncertainties were tested with one and two-way deterministic and probabilistic Monte Carlo sensitivity analysis. Results: SF gave 23.72 months (18.51 QALMs) versus 20.22 months (16.26 QALMs). Markov Cohort Analysis showed that where all treatment modalities were received NAT gave 35.05 months (29.87 QALMs) versus 30.96 months (24.86QALMs) for R0 resection and 34.08 months (29.87 QALMs) versus 25.85 months (20.72 QALMs) for R1 resection. One-way deterministic sensitivity analysis showed that NAT was superior if the resection rate was greater than 51.04% or below 75.68% in SF pathway. Two-way sensitivity analysis showed that pathway superiority depended on obtaining multimodal treatment in either pathway. Conclusion: Whilst NAT is a viable alternative to traditional SF approach, superior pathway selection depends on the individual patient’s likelihood of receiving multimodal treatment in either pathway. [ABSTRACT FROM AUTHOR]

Syphilis is a Sexually Transmitted Infection (IST) with significant importance to public health, due to its impact during pregnancy (Gestational Syphilis—GS); especially because syphilis can affect fetus and neonates’ development (mother-to-child transmission—MTCT of syphilis), by increasing susceptibility to abortion, premature birth, skeletal malformations, meningitis and pneumonia. Measures to control and eliminate MTCT of syphilis have failed on the last few years in Brazil and this research aimed to identify the seasonality of notified cases of syphilis in a region of São Paulo state. The studied region, Pontal do Paranapanema, comprises 32 cities located in the West of São Paulo state, in Brazil. Data collected from the National System of Aggravations and Notification (SINAN) website was used to calculate the incidence rate of GS and MTCT. The incidence rate of GS was acquired dividing number of cases by number of women in each municipality and MTCT using number of live births in each year (from 2007 to 2013) in each municipality. This result was then, standardized multiplying incidence rate by 10,000 and expressed as incidence/10,000 women or live births, for GS and MTCT, respectively. To identify possible endemic/epidemic periods, a control diagram was performed using the standard deviation (SD) of incidence rate. Thematic maps representing the spatial distribution of incidence rates were constructed using a Geographic Information System software (GIS, based on cartographic vector available on the Brazilian Institute of Geography and Statistics (IBGE) website. Eighty cases of GS and 61 cases of MTCT were notified in the studied region. An increase of GS notification was detected in the Pontal do Paranapanema in 2011 followed by an increase in number of MTCT cases in the subsequent year, suggesting inefficacy in the treatment during gestational period. Most of those cases were reported on February and November which suggested seasonality for this IST in the region. The control diagram, based on the inputs collected from SINAN, showed no endemic period; however, the most susceptible month to happen an endemic event of GS and MTCT was February. Our study provided a new methodology to understand the syphilis dynamics as a potential tool to improve the success of future measures to control and possibly eliminate MTCT of syphilis. [ABSTRACT FROM AUTHOR]

Introduction: At the end of 2015, the first cases of Zika were identified in southern Mexico. During 2016, Zika spread as an outbreak to a large part of the country's coastal zones. Methodology: The Zika epidemiological surveillance system records cases with clinical symptoms of Zika virus disease (ZVD) and those confirmed by means of a reverse polymerase chain reaction (RT-PCR) assay. This report includes the suspected and confirmed cases from 2016. Incidence rates were estimated by region and in pregnant women based on the proportion of confirmed cases. Results: In total, 43,725 suspected cases of ZVD were reported. The overall incidence of suspected cases of ZVD was 82.0 per 100,000 individuals and 25.3 per 100,000 Zika cases. There were 4,168 pregnant women with suspected symptoms of ZVD, of which infection was confirmed in 1,082 (26%). The estimated incidence rate of ZVD for pregnant women nationwide was 186.1 positive Zika cases per 100,000 pregnant women. Conclusions: The incidence of Zika in Mexico is higher than that reported previously in the National System of Epidemiological Surveillance. Positive cases of Zika must be estimated and reported. [ABSTRACT FROM AUTHOR]

Real-time forecasts based on mathematical models can inform critical decision-making during infectious disease outbreaks. Yet, epidemic forecasts are rarely evaluated during or after the event, and there is little guidance on the best metrics for assessment. Here, we propose an evaluation approach that disentangles different components of forecasting ability using metrics that separately assess the calibration, sharpness and bias of forecasts. This makes it possible to assess not just how close a forecast was to reality but also how well uncertainty has been quantified. We used this approach to analyse the performance of weekly forecasts we generated in real time for Western Area, Sierra Leone, during the 2013–16 Ebola epidemic in West Africa. We investigated a range of forecast model variants based on the model fits generated at the time with a semi-mechanistic model, and found that good probabilistic calibration was achievable at short time horizons of one or two weeks ahead but model predictions were increasingly unreliable at longer forecasting horizons. This suggests that forecasts may have been of good enough quality to inform decision making based on predictions a few weeks ahead of time but not longer, reflecting the high level of uncertainty in the processes driving the trajectory of the epidemic. Comparing forecasts based on the semi-mechanistic model to simpler null models showed that the best semi-mechanistic model variant performed better than the null models with respect to probabilistic calibration, and that this would have been identified from the earliest stages of the outbreak. As forecasts become a routine part of the toolkit in public health, standards for evaluation of performance will be important for assessing quality and improving credibility of mathematical models, and for elucidating difficulties and trade-offs when aiming to make the most useful and reliable forecasts. [ABSTRACT FROM AUTHOR]

Measles is a highly infectious, severe viral disease. The disease is targeted for global eradication; however, this result has proven challenging. In China, where countrywide vaccination coverage for the last decade has been above 95% (the threshold for measles elimination), measles continues to cause large epidemics. To diagnose factors contributing to the persistency of measles, here we develop a model-inference system to infer measles transmission dynamics in China. The model-inference system uses demographic and vaccination data for each year as model inputs to directly account for changing population dynamics (including births, deaths, migrations, and vaccination). In addition, it simultaneously estimates unobserved model variables and parameters based on incidence data. When fitted to yearly incidence data for the entire population, it is able to accurately estimate independent, out-of-sample age-specific incidence. Using this validated model-inference system, we are thus able to estimate epidemiological and demographical characteristics key to measles transmission during 1951–2004 for three key locations in China, including its capital Beijing. These characteristics include age-specific population susceptibility and incidence rates, the basic reproductive number (R0), reporting rate, population mixing intensity, and amplitude of seasonality. Key differences among the three sites reveal population and epidemiological characteristics crucial for understanding the current persistence of measles epidemics in China. We also discuss the implications our findings have for future elimination strategies. [ABSTRACT FROM AUTHOR]

We characterized 419 Mycoplasma pneumoniae isolates collected between 2011 and 2017 in Osaka prefecture of Japan. This analysis revealed high prevalence of macrolide-resistant M. pneumoniae (MRMP) in Osaka during 2011 and 2014 with annual detection rates of MRMP strains between 71.4% and 81.8%. However, in 2015 and after, the detection rate of MRMP decreased significantly and did not exceed 50%. Genotyping of the p1 gene of these isolates showed that most of MRMP strains harbored type 1 p1 gene. In contrast, strains expressing p1 gene type 2 or its variant were largely macrolide-susceptible M. pneumoniae (MSMP) strains. There was a strong correlation between p1 gene genotype and the presence of mutations conferring macrolide resistance in M. pneumoniae isolated in Osaka. These results indicate that lower incidence of MRMP strains in Osaka from 2015 was associated with the relative increase of p1 gene type 2 lineage strains. During these experiments, we also isolated three M. pneumoniae strains that showed irregular typing pattern in the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) analysis of the p1 gene. Two of these strains harbored new variants of type 2 p1 gene and were designated as type 2f and 2g. The remaining strain with an irregular typing pattern had a large deletion in the p1 operon. [ABSTRACT FROM AUTHOR]

Infectious disease transmission in animals is an inherently spatial process in which a host’s home location and their social mixing patterns are important, with the mixing of infectious individuals often different to that of susceptible individuals. Although incidence data for humans have traditionally been aggregated into low-resolution data sets, modern representative surveillance systems such as electronic hospital records generate high volume case data with precise home locations. Here, we use a gridded spatial transmission model of arbitrary resolution to investigate the theoretical relationship between population density, differential population movement and local variability in incidence. We show analytically that a uniform local attack rate is only possible for individual pixels in the grid if susceptible and infectious individuals move in the same way. Using a population in Guangdong, China, for which a robust quantitative description of movement is available (a movement kernel), and a natural history consistent with pandemic influenza; we show that local cumulative incidence is positively correlated with population density when susceptible individuals are more connected in space than infectious individuals. Conversely, under the less intuitively likely scenario, when infectious individuals are more connected, local cumulative incidence is negatively correlated with population density. The strength and direction of correlation changes sign for other kernel parameter values. We show that simulation models in which it is assumed implicitly that only infectious individuals move are assuming a slightly unusual specific correlation between population density and attack rate. However, we also show that this potential structural bias can be corrected by using the appropriate non-isotropic kernel that maps infectious-only code onto the isotropic dual-mobility kernel. These results describe a precise relationship between the spatio-social mixing of infectious and susceptible individuals and local variability in attack rates. More generally, these results suggest a genuine risk that mechanistic models of high-resolution attack rate data may reach spurious conclusions if the precise implications of spatial force-of-infection assumptions are not first fully characterized, prior to models being fit to data. [ABSTRACT FROM AUTHOR]

Substantial progress has been made towards the goal of ending the HIV/AIDS epidemic due to advancements in both prevention and treatment of HIV. However, major challenges still remain. We describe basic principles of epidemic control in the context of HIV and identify a number of attainable goals in terms of control and elimination of HIV in specific populations and risk groups, given currently available HIV prevention and treatment methods. Currently available HIV prevention methods make it a feasible goal to eliminate HIV transmission attributable to mother-to-child transmission and blood transfusions. Reductions in transmission attributable to sexual behavior and injection drug use are feasible, but elimination of these modes of transmission will require further advancements in behavioral and biomedical HIV prevention. With regard to HIV-related mortality, we argue that elimination of death due to HIV-related causes is a feasible goal. HIV-related deaths should be treated as sentinel events triggering epidemiological investigation into the breakdowns in the HIV care continuum that led to them. We briefly discuss additional considerations that will affect the success of HIV prevention programs. [ABSTRACT FROM AUTHOR]

The article offers information on the study of risk of autochthonous yellow fever transmission in Lazio, Italy. Topics discussed include stochastic transmission model adapted to model Yellow fever virus (YFV) transmission; causes of transmission of YFV involves infected travelers and Aedes albopictus populations; and result of the study that says low, but no negligible risk of YFV transmission in European areas.

We studied trends in the incidence of health care-associated infections (HAIs) in LTCFs between 2009 and 2015 and determined the effect of participation in our network. Eldercare physicians reported weekly the number of cases of influenza-like illness, gastroenteritis, (probable) pneumonia, urinary tract infections (UTIs) and all-cause mortality. Trends in the incidence of infection and mortality in relation to LTCF characteristics were calculated using multilevel univariate and multivariate logistic regression. Thirty LTCF participated for 3 years or more, 16 for 2 years and the remaining 12 LTCF for 1 year. During the study period, the median number of beds decreased from 158 to 139, whereas the percentage of residents with private bedrooms increased from 14% to 87%. UTIs were the most frequently reported infections, followed by (probable) pneumonia and gastroenteritis. Adjusted for calendar year and season, we observed a statistically significant decrease in the incidence of influenza-like illness (odds ratio (OR) = 0.8, P < 0.01) and (probable) pneumonia (OR = 0.8, P < 0.01) for each extra year an LTCF participated. Although there are other likely contributors, such as more private rooms and enhanced infection control measures, the decreasing trend of HAI in LTCFs participating in surveillance implies that surveillance is a valuable addition to current strategies to optimise infection control. [ABSTRACT FROM AUTHOR]

Here we wanted to assess whether sexual risk behaviour differs dependent by human immunodeficiency virus (HIV) status by following 100 HIV- and 137 HIV+ women recruited at two university teaching hospitals in Rwanda. Women were tested for sexually transmitted infections (STIs; trichomoniasis, syphilis, hepatitis B and C) and for reproductive tract infections (RTIs; candidiasis, bacterial vaginosis (BV)) and were interviewed at baseline and 9 months later. BV was the most prevalent infection, while syphilis was the most common STI with a 9-month incidence of 10.9% in HIV+ women. Only 24.5% of women positive for any RTI/STI contacted their health facility and got treatment. More HIV- women than HIV+ women had had more than one sexual partner and never used condoms during the follow-up period. The use of condoms was affected neither by marital status nor by concomitant STIs besides HIV. Our data highlight the importance of public education regarding condom use to protect against STIs in an era when HIV no longer is a death sentence. [ABSTRACT FROM AUTHOR]

Significant ethnic and socio-economic disparities exist in infectious diseases (IDs) rates in New Zealand, so accurate measures of these characteristics are required. This study compared methods of ascribing ethnicity and socio-economic status. Children in the Growing Up in New Zealand longitudinal cohort were ascribed to self-prioritised, total response and single-combined ethnic groups. Socio-economic status was measured using household income, and both census-derived and survey-derived deprivation indices. Rates of ID hospitalisation were compared using linked administrative data. Self-prioritised ethnicity was simplest to use. Total response accounted for mixed ethnicity and allowed overlap between groups. Single-combined ethnicity required aggregation of small groups to maintain power but offered greater detail. Regardless of the method used, Māori and Pacific children, and children in the most socio-economically deprived households had a greater risk of ID hospitalisation. Risk differences between self-prioritised and total response methods were not significant for Māori and Pacific children but single-combined ethnicity revealed a diversity of risk within these groups. Household income was affected by non-random missing data. The census-derived deprivation index offered a high level of completeness with some risk of multicollinearity and concerns regarding the ecological fallacy. The survey-derived index required extra questions but was acceptable to participants and provided individualised data. Based on these results, the use of single-combined ethnicity and an individualised survey-derived index of deprivation are recommended where sample size and data structure allow it. [ABSTRACT FROM AUTHOR]

Acute lower respiratory infections (ALRI) are a leading cause of morbidity and mortality globally, with most ALRI deaths occurring in children in developing countries. Computational models can be used to test the efficacy of respiratory infection prevention interventions, but require data on social mixing patterns, which are sparse in developing countries. We describe social mixing patterns among a rural community in northern India. During October 2015-February 2016, trained field workers conducted cross-sectional face-to-face standardized surveys in a convenience sample of 330 households in Faridabad District, Haryana State, India. Respondents were asked about the number, duration, and setting of social interactions during the previous 24 hours. Responses were compared by age and gender. Among the 3083 residents who were approached, 2943 (96%) participated, of whom 51% were male and the median age was 22 years (interquartile range (IQR) 9–37). Respondents reported contact (defined as having had a face-to-face conversation within 3 feet, which may or may not have included physical contact) with a median of 17 (IQR 12–25) people during the preceding 24 hours. Median total contact time per person was 36 person-hours (IQR 26–52). Female older children and adults had significantly fewer contacts than males of similar age (Kruskal-Wallis χ2 = 226.59, p<0.001), but spent a longer duration in contact with young children (Kruskal-Wallis χ2 = 27.26, p<0.001), suggesting a potentially complex pattern of differential risk of infection between genders. After controlling for household size and day of the week, respondent age was significantly associated with number and duration of contacts. These findings can be used to model the impact of interventions to reduce lower respiratory tract infections in India. [ABSTRACT FROM AUTHOR]

In a Policy Forum, Joseph Amon and colleagues discuss human rights indicators for tracking progress toward ending the HIV epidemic. [ABSTRACT FROM AUTHOR]

Background: The required efforts, feasibility and predicted success of an intervention strategy against an infectious disease are partially determined by its basic reproduction number, R0. In its simplest form R0 can be understood as the product of the infectious period, the number of infectious contacts and the per-contact transmission probability, which in the case of vector-transmitted diseases necessarily extend to the vector stages. As vectors do not usually recover from infection, they remain infectious for life, which places high significance on the vector’s life expectancy. Current methods for estimating the R0 for a vector-borne disease are mostly derived from compartmental modelling frameworks assuming constant vector mortality rates. We hypothesised that some of the assumptions underlying these models can lead to unrealistic high vector life expectancies with important repercussions for R0 estimates. Methodology and principal findings: Here we used a stochastic, individual-based model which allowed us to directly measure the number of secondary infections arising from one index case under different assumptions about vector mortality. Our results confirm that formulas based on age-independent mortality rates can overestimate R0 by nearly 100% compared to our own estimate derived from first principles. We further provide a correction factor that can be used with a standard R0 formula and adjusts for the discrepancies due to erroneous vector age distributions. Conclusion: Vector mortality rates play a crucial role for the success and general epidemiology of vector-transmitted diseases. Many modelling efforts intrinsically assume these to be age-independent, which, as clearly demonstrated here, can lead to severe over-estimation of the disease’s reproduction number. Our results thus re-emphasise the importance of obtaining field-relevant and species-dependent vector mortality rates, which in turn would facilitate more realistic intervention impact predictions. [ABSTRACT FROM AUTHOR]