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Zika Virus Outbreak, Barbados, 2015 - 2016

Barbados is a Caribbean island country of approximately 285,000 people, with a thriving tourism industry. In 2015, Zika spread rapidly throughout the Americas, and its proliferation through the Caribbean islands followed suit. Barbados reported its first confirmed autochthonous Zika transmission to the Pan American Health Organization (PAHO) in January 2016, a month before the global public health emergency was declared. Following detection of suspected Zika cases on Barbados in 2015, 926 individuals were described as suspected cases, and 147 lab confirmed cases were reported through December 2016, the end of the most recent epidemiological year. In this short report, we describe the epidemiological characteristics of 926 clinical case records which were originally suspected as cases of Zika, and which were subsequently sent for testing and confirmation; 147 were found positive for Zika, using RT-PCR methods, another 276 tested negative, and the remaining 503 were either pending results or still in the suspected category. Women were represented at about twice the rate of men in case records where sex was reported (71.9%), and confirmed cases (78.2%), and 19 of the confirmed positive cases were children under the age of 10.

epidemiology↗

Characterisation of carried and invasive Neisseria meningitidis isolates in Shanghai, China from 1950 to 2016: implications for serogroup B vaccine implementation

BackgroundSerogroup B invasive meningococcal disease (IMD) is increasing in China, little is known however, about these meningococci. This study characterises a collection of isolates associated with IMD and carriage in Shanghai and assesses current vaccine strategies.\n\nMethodsIMD epidemiological data in Shanghai from 1950-2016 were obtained from the National Notifiable Diseases Registry System, with 460 isolates collected for analysis including, 169 from IMD and 291 from carriage. Serogroup B meningococcal (MenB) vaccine coverage was evaluated using Bexsero(R) Antigen Sequence Type (BAST).\n\nResultsSeven IMD epidemic periods have been observed in Shanghai since 1950, with incidence peaking from February to April. Analyses were divided according to the period of meningococcal polysaccharide vaccine (MPV) introduction: (i) pre-MPV-A, 1965-1980; (ii) post-MPV-A, 1981-2008; and (iii) post-MPV-A+C, 2009-2016. IMD incidence decreased from 55.4/100,000 to 0.71 then to 0.02, and corresponded with shifts from serogroup A ST-5 complex (MenA:cc5) to MenC:cc4821 then MenB:cc4821. MenB IMD became predominant (63.2%) in the post-MPV-A+C period, of which 50% were caused by cc4821, with the highest incidence in infants (0.45/100,000) and a case-fatality rate of 9.5%. IMD was positively correlated with carriage rates. Data indicate that fewer than 25% of MenB isolates in the post-MPV-A+C period may be covered by the vaccines Bexsero(R), Trumenba(R), or a PorA-based vaccine, NonaMen.\n\nConclusionsA unique IMD epidemiology is found in China, changing periodically from hyperepidemic to low-level endemic disease. MenB IMD now dominates in Shanghai, with isolates harbouring diverse antigenic variants potentially beyond coverage with licenced OMV- and protein-based MenB vaccines.\n\nSummaryMeningococcal disease in Shanghai, China is described and current vaccine approaches evaluated. Since 1950, MenA:cc5 shifted to MenC:cc4821 then MenB:cc4821, with MenB dominating since 2009. Distinct antigens potentially beyond coverage with licensed OMV- and protein-based MenB vaccines were found.

epidemiology↗

Network-based assessment of the vulnerability of Italian regions to bovine brucellosis

The endemic circulation of bovine brucellosis in cattle herds has a markedly negative impact on economy, due to decreased fertility, increased abortion rates, reduced milk and meat production. It also poses a direct threat to human health. In Italy, despite the long lasting efforts and the considerable economic investment, complete eradication of this disease still eludes the southern regions, as opposed to the northern regions that are disease-free. Here we introduced a novel quantitative network-based approach able to fully exploit the highly resolved databases of cattle trade movements and outbreak reports to yield estimates of the vulnerability of a cattle market to brucellosis. Tested on the affected regions, the introduced vulnerability indicator was shown to be accurate in predicting the number of bovine brucellosis outbreaks, thus confirming the suitability of our tool for epidemic risk assessment. We evaluated the dependence of regional vulnerability to brucellosis on a set of factors including premises spatial distribution, trading patterns, farming practices, herd market value, compliance to outbreak regulations, and exploring different epidemiological conditions. Animal trade movements were identified as a major route for brucellosis spread between farms, with an additional potential risk attributed to the use of shared pastures. By comparing the vulnerability of disease-free regions in the north to affected regions in the south, we found that more intense trade and higher market value of the cattle sector in the north, likely inducing more efficient biosafety measures, together with poor compliance to trade restrictions following outbreaks in the south were key factors explaining the diverse success in eradicating brucellosis. Our modeling scheme is both synthetic and effective in gauging regional vulnerability to brucellosis persistence. Its general formulation makes it adaptable to other diseases and host species, providing a useful tool for veterinary epidemiology and policy assessment.

epidemiology↗

Community origins and regional differences in plasmid-mediated fluoroquinolone resistant Enterobacteriaceae infections in children

BackgroundFluoroquinolones (FQs) are uncommonly prescribed in children, yet pediatric multidrug-resistant (MDR)-Enterobacteriaceae (Ent) infections often reveal FQ resistance (FQR). We sought to define the molecular epidemiology of FQR and MDR-Ent in children.\n\nMethodsA case-control analysis of children with MDR-Ent infections at 3 Chicago hospitals was performed. Cases were children with third-generation-cephalosporin-resistant (3GCR) and/or carbapenem-resistant (CR)-Ent infections. PCR and DNA analysis assessed bla and plasmid-mediated FQR (PMFQR) genes. Controls were children with 3GC and carbapenem susceptible-Ent infections matched by age, source and hospital. We assessed clinical-epidemiologic predictors of PMFQR Ent infection.\n\nResultsOf 169 3GCR and/or CR Ent isolates from children (median age 4.8 years), 85 were FQR; 56 (66%) contained PMFQR genes. The predominant organism was E. coli and most common bla gene bla CTX-M-1 group. In FQR isolates, PMFQR gene mutations included aac61b-cr, oqxA/B, qepA, and qnrA/B/D/S in 83%, 15%, 13% and 11% of isolates, respectively. FQR E. coli was often associated with phylogroup B2, ST43/ST131. On multivariable analysis, PMFQR Ent infections occurred mostly in outpatients (OR 33.1) of non-black-white-Hispanic race (OR 6.5). Residents of Southwest Chicago were >5 times more likely to have PMFQR-Ent infections than those in the reference region, while residence in Central Chicago was associated with a 97% decreased risk. Other demographic, comorbidity, invasive-device, antibiotic use, or healthcare differences were not found.\n\nConclusionsThe strong association of infection with MDROs showing FQR with patient residence rather than with traditional risk factors suggests that the community environment is a major contributor to spread of these pathogens in children.

epidemiology↗

Unsupervised Extraction of Epidemic Syndromes from Participatory Influenza Surveillance Self-reported Symptoms

Seasonal influenza surveillance is usually carried out by sentinel general practitioners who compile weekly reports based on the number of influenza-like illness (ILI) clinical cases observed among visited patients. This practice for surveillance is generally affected by two main issues: i) reports are usually released with a lag of about one week or more, ii) the definition of a case of influenza-like illness based on patients symptoms varies from one surveillance system to the other, i.e. from one country to the other. The availability of novel data streams for disease surveillance can alleviate these issues; in this paper, we employed data from Influenzanet, a participatory web-based surveillance project which collects symptoms directly from the general population in real time. We developed an unsupervised probabilistic framework that combines time series analysis of symptoms counts and performs an algorithmic detection of groups of symptoms, hereafter called syndromes. Symptoms counts were collected through the participatory web-based surveillance platforms of a consortium called Influenzanet which is found to correlate with Influenza-like illness incidence as detected by sentinel doctors. Our aim is to suggest how web-based surveillance data can provide an epidemiological signal capable of detecting influenza-like illness temporal trends without relying on a specific case definition. We evaluated the performance of our framework by showing that the temporal trends of the detected syndromes closely follow the ILI incidence as reported by the traditional surveillance, and consist of combinations of symptoms that are compatible with the ILI definition. The proposed framework was able to predict quite accurately the ILI trend of the forthcoming influenza season based only on the available information of the previous years. Moreover, we assessed the generalisability of the approach by evaluating its potentials for the detection of gastrointestinal syndromes. We evaluated the approach against the traditional surveillance data and despite the limited amount of data, the gastrointestinal trend was successfully detected. The result is a real-time flexible surveillance and prediction tool that is not constrained by any disease case definition.\n\nAuthor summaryThis study suggests how web-based surveillance data can provide an epidemiological signal capable of detecting influenza-like illness temporal trends without relying on a specific case definition. The proposed framework was able to predict quite accurately the ILI trend of the forthcoming influenza season based only on the available information of the previous years. Moreover, we assessed the generalisability of the approach by evaluating its potentials for the detection of gastrointestinal syndromes. We evaluated the approach against the traditional surveillance data and despite the limited amount of data, the gastrointestinal trend was successfully detected. The result is a real-time flexible surveillance and prediction tool that is not constrained by any disease case definition.

epidemiology↗

A community-level investigation of the yellow fever virus outbreak in South Omo Zone, South-West Ethiopia, 2012-2014

BackgroundA yellow fever (YF) outbreak occurred in South Omo Zone, Ethiopia in 2012-2014. This study aimed to analyse historical epidemiological data, to assess the risk for future YF outbreaks through entomological surveillance, including mosquito species identification and molecular screening for arboviruses, and finally to determine the knowledge, attitudes and current preventative practices within the affected communities.\n\nMethodology/Principal FindingsFrom October 2012 to March 2014, 165 cases and 62 deaths were reported, principally in rural areas of South Ari region (83.6%), south-west Ethiopia. The majority of patients were 15-44 years old (74.5%) and most case deaths were males (76%). Between June and August 2017, 688 containers were sampled from across 177 households to identify key breeding sites for Aedes mosquitoes. Ensete ventricosum (\"false banana\") was identified as the primary natural breeding site, and clay pots outside the home as the most productive artificial breeding site. Entomological risk indices from the majority of sites were classified as \"high risk\" for future outbreaks under current World Health Organization criteria. Adult trapping resulted in the identification of members of the Aedes simpsoni complex in and around households. Screening of adult females revealed no detection of yellow fever virus (YFV) or other arboviruses. 88% of 177 participants had heard of YF, however many participants easily confused transmission and symptoms of YF with malaria, which is also endemic in the area.\n\nConclusions/SignificanceStudy results emphasise the need for further entomological studies to improve our understanding of local vector species and transmission dynamics. Disease surveillance systems and in-country laboratory capacity also need to be strengthened to facilitate more rapid responses to future YF outbreaks.\n\nAuthor SummaryDespite the availability of a highly effective vaccine, yellow fever virus (YFV) remains an important public health problem across Africa and South America due to its high case-fatality rate. This study aimed to assess and reduce the risk for future outbreaks. During this study, historical data analysis was conducted to understand the epidemiology of the recent outbreak in 2012-2014. Entomological surveillance was also carried out, including both mosquito species identification and molecular screening for arboviruses, as well as a household survey to understand the knowledge and attitudes towards yellow fever (YF) within the affected areas and to assess community-level practices for YF prevention. We found a high abundance of Aedes simpsoni complex in the context of low vaccination coverage. Community knowledge and practice levels were low for reducing potential breeding sites, highlighting the need for increased dissemination of information to community members on how to reduce their risk of exposure to mosquito vectors of arboviruses.

epidemiology↗

Concurrent Spatiotemporal Daily Land Use Regression Modeling and Missing Data Imputation of Fine Particulate Matter Using Distributed Space Time Expectation Maximization

Graphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC=\"FIGDIR/small/354852_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (37K):\norg.highwire.dtl.DTLVardef@caef72org.highwire.dtl.DTLVardef@12e5f38org.highwire.dtl.DTLVardef@16d6379org.highwire.dtl.DTLVardef@9d9daa_HPS_FORMAT_FIGEXP M_FIG C_FIG Land use regression (LUR) has been widely applied in epidemiologic research for exposure assessment. In this study, for the first time, we aimed to develop a spatiotemporal LUR model using Distributed Space Time Expectation Maximization (D-STEM). This spatiotemporal LUR model examined with daily particulate matter [≤] 2.5 m (PM2.5) within the megacity of Tehran, capital of Iran. Moreover, D-STEM missing data imputation was compared with mean substitution in each monitoring station, as it is equivalent to ignoring of missing data, which is common in LUR studies that employ regulatory monitoring stations data. The amount of missing data was 28% of the total number of observations, in Tehran in 2015. The annual mean of PM2.5 concentrations was 33 g/m3. Spatiotemporal R-squared of the D-STEM final daily LUR model was 78%, and leave-one-out cross-validation (LOOCV) R-squared was 66%. Spatial R-squared and LOOCV R-squared were 89% and 72%, respectively. Temporal R-squared and LOOCV R-squared were 99.5% and 99.3%, respectively. Mean absolute error decreased 26% in imputation of missing data by using the D-STEM final LUR model instead of mean substitution. This study reveals competence of the D-STEM software in spatiotemporal missing data imputation, estimation of temporal trend, and mapping of small scale (20 x 20 meters) within-city spatial variations, in the LUR context. The estimated PM2.5 concentrations maps could be used in future studies on short- and/or long-term health effects. Overall, we suggest using D-STEM capabilities in increasing LUR studies that employ data of regulatory network monitoring stations.\n\nHighlights- First Land Use Regression using D-STEM, a recently introduced statistical software\n- Assess D-STEM in spatiotemporal modeling, mapping, and missing data imputation\n- Estimate high resolution (20x20 m) daily maps for exposure assessment in a megacity\n- Provide both short- and long-term exposure assessment for epidemiological studies

epidemiology↗

Clinico-Demographic trend of HIV-positive cases and sero-discordance at a secondary level hospital in Haryana, North India- programmatic implications for a low HIV prevalence State.

BackgroundAppropriate programmatic intervention for HIV Care and Treatment in a low prevalence state requires local level analysis of programme data. Data generated at an Integrated Counseling and Testing Centre (ICTC) may provide crucial information to understand the epidemiology of the disease in a particular region. There is paucity of information on HIV epidemiology at sub-district level in a low HIV prevalence State of India.\n\nMethodsA secondary analysis of the records from January to December for the years, 2009 through 2014 was conducted among clients who tested HIV positive at the ICTC of a sub-district hospital in Haryana, North India.\n\nResultsA total of 199 individuals were tested HIV positive of whom 121 (61%) were males. By age-group, 8, 8, 178, and 5 individuals were respectively in <5, 5-18, 18-59 and >60 years of age. Over years from 2009 through 2014, 11, 12, 30, 37, 51 and 58 people tested HIV positive, with no sigfinicant sex difference (chi2 p =0.929). Statistically non-significant increase of 18-59 years individuals was observed, from zero in 2009 to 5 in 2014. Major route of transmission was heterosexual (80%), followed-by, parent-to-child (5%), Blood Transfusion (1.5%), MSM (1%) and FSW (0.5%). One third each, were self-referred, from government facility; 16% from tuberculosis clinic. Median CD4 count in 2014, was 392. Serodiscordance rate spouses of HIV positive females was 17%, of males was 33%.\n\nConclusionAnalysis of programme data at a sub-district ICTC could highlight emerging trend even in a low HIV prevalence state.

epidemiology↗

The impact of behavioral interventions on co-infectiondynamics: an exploration of the effects of home isolation

Behavioral changes due to the development of symptoms have been studied in mono-infections. However, in reality, multiple infections are circulating within the same time period and behavioral changes resulting from contraction of one of the diseases affect the dynamics of the other.\n\nThe present study aims at assessing the effect of home isolation on the joint dynamics of two infectious diseases, including co-infection, assuming that the two diseases do not confer cross-immunity. We use an age- and time- structured co-infection model based on partial differential equations. Social contact matrices, describing different mixing patterns of symptomatic and asymptomatic individuals are incorporated into the calculation of the age- and time-specific marginal forces of infection.\n\nTwo scenarios are simulated, assuming that one of the diseases has more severe symptoms than the other. In the first scenario, people stay only at home when having symptoms of the most severe disease. In the second scenario, twice as many people stay at home when having symptoms of the most severe disease than when having symptoms of the other disease.\n\nThe results show that the impact of home isolation on the joint dynamics of two infectious diseases depends on the epidemiological parameters and properties of the diseases (e.g., basic reproduction number, symptom severity). In case both diseases have a low to moderate basic reproduction number, and there is no home isolation for the less severe disease, the final size of the less severe disease increases with the proportion of symptomatic cases of the most severe disease staying at home, after an initial decrease. When twice as many people stay at home when having symptoms of the most severe disease than when having symptoms of the other disease, increasing the proportion staying at home always reduces the final size of both diseases, and the number of co-infections.\n\nIn conclusion, when providing advise if people should stay at home in the context of two or more co-circulating diseases, one has to take into account epidemiological parameters and symptom severity.

epidemiology↗

DTK-Dengue: A new agent-based model of dengue virus transmission dynamics

Dengue virus (DENV) is a pathogen spread by Aedes mosquitoes that has a considerable impact on global health. Agent-based models can be used to explicitly represent factors that are difficult to measure empirically, by focusing on specific aspects of DENV transmission dynamics that influence spread in a particular location. We present a new agent-based model for DENV dynamics, DTK-Dengue, that can be readily applied to new locations and to a diverse set of goals. It extends the vector-borne disease module in the Institute for Disease Modellings Epidemiological Modeling Disease Transmission Kernel (EMOD-DTK) to model DENV dynamics. There are three key modifications present in DTK-Dengue: 1) modifications to how climatic variables influence vector development for Aedes mosquitoes, 2) updates to adult vector behavior to make them more similar to Aedes, and 3) the inclusion of four DENV serotypes, including their effects on human immunity and symptoms. We demonstrate DTK-Dengues capabilities by fitting the model to four interrelated datasets: total and serotype-specific dengue incidences between January 2007 and December 2008 from San Juan, Puerto Rico; the age distribution of reported dengue cases in Puerto Rico during 2007; and the number of adult female Ae. aegypti trapped in two neighborhoods of San Juan between November 2007 and December 2008. The model replicated broad patterns in the reference data, including a correlation between vector population dynamics and rainfall, appropriate seasonality in the reported incidence, greater circulation of DENV-3 than any other serotype, and an inverse relationship between age and the proportion of cases associated with each age group over 20 years old. This exercise demonstrates the potential for DTK-Dengue to assimilate multiple types of epidemiologic data into a realistic portrayal of DENV transmission dynamics. Due to the open availability of the DTK-Dengue software and the availability of numerous other modules for modeling disease transmission and control from EMOD-DTK, this new model has potential for a diverse range of future applications in a wide variety of settings.

epidemiology↗

A single test approach for accurate and sensitive detection and taxonomic characterization of Trypanosomes by comprehensive analysis of ITS1 amplicons

The World Health Organization has targeted stopping the transmission of Human African Trypanosomiasis by 2030. To achieve this, better tools are urgently required to identify and monitor Trypanosome infections in human, animals, and tsetse fly vectors. This study presents a single test approach for detection and identification of Trypanosomes and their comprehensive characterization at species and sub-group level. Our method uses newly designed ITS1 PCR primers (a widely used method for detection of African Trypanosomes, amplifying the ITS1 region of ribosomal RNA genes) coupled to Illumina sequencing of the amplicon. The protocol is based on the widely used Illuminas 16s bacterial metagenomic analysis procedure that makes use of multiplex PCR and dual indexing. We analyzed wild tsetse flies collected from Zambia and Zimbabwe. Our results show that the traditional method for Trypanosome species detection based on band size comparisons on a gel is unable to distinguish between T. vivax and T. godfreyi accurately. Additionally, this approach shows increased sensitivity of detection at species level. Through phylogenetic analysis, we identified Trypanosomes at species and sub-group level without the need for any additional tests. Our results show T. congolense Kilifi sub-group is more closely related to T. simiae than to other T. congolense sub-groups. This agrees with previous studies using satellite DNA and 18s RNA analysis. While current classification does not list any sub-groups for T. vivax and T. godfreyi, we observed distinct subgroups for these species. Interestingly, sequences matching T. congolense Tsavo (now classified as T. simiae Tsavo) clusters distinctly from the rest of the T. simiae Tsavo sequences suggesting that the Nannomonas group is more divergent than currently thought thus the need for a better classification criteria. This approach has the potential for refining classification of Trypanosomes and provide detailed molecular epidemiology information useful for surveillance and transmission control efforts.\n\nAuthor summaryDetection of Trypanosomes in the tsetse flies plays an important role in the control of African trypanosomiasis by providing information on circulating Trypanosome species in a given area. We have developed a method that combines multiplex PCR and next-generation sequencing for Trypanosome species detection. The method is based on the widely used bacterial metagenomic analysis protocol and uses a modular, two-step PCR process followed by sequencing of all amplicons in a single run, making sequencing of amplicons more efficient and cost-effective when dealing with large sample sizes. As part of this approach, we designed novel primers for amplifying the ITS1 region of the Trypanosome rRNA gene that is more sensitive than conventional primers. Identification of Trypanosome species is based on BLAST searches against the constantly updated NCBIs nt database, which facilitates the identification of Trypanosome subgroups. Our approach is more accurate than traditional gel-based analysis and shows how the latter is prone to misidentification. It is also sensitive and is able to discriminate between subgroups within Trypanosome species. Applied as an epidemiological tool, it has the potential to provide new, comprehensive and more accurate information on vector-pathogen-host interconnections which are key in the control and management of African trypanosomiasis.

epidemiology↗

The geography of malaria elimination in Bangladesh: combining data layers to estimate the spatial spread of parasites

Malaria control programs face difficult resource allocation decisions. Of particular concern for countries aiming for malaria elimination, the regular movement of individuals to and from endemic areas undermines local interventions by reintroducing infections and sustaining local transmission. Quantifying this movement of malaria parasites around a country has become a priority for national control programs, but remains methodologically challenging, particularly in areas with highly mobile populations. Here, we combined multiple data sources to measure the geographical spread of malaria parasites, including epidemiological surveillance data, travel surveys, parasite genetic data, and anonymized mobile phone data. We collected parasite genetic barcodes and travel surveys from 2,090 patients residing in 176 unions in southeast Bangladesh. We developed a genetic mixing index to quantify the likelihood of samples being local or imported. We then inferred the direction and intensity of parasite flow between locations using an epidemiological model, and estimated the proportion of imported cases assuming mobility patterns parameterized using the travel survey and mobile phone calling data. Our results show that each data source provided related but different information about the patterns of geographic spread of parasites. We identify a consistent north/south separation of the Chittagong Hill Tracts region in Bangladesh, and found that in addition to imported infections from forested regions, frequent mixing also occurs in low transmission but highly populated areas in the southwest. Thus, unlike risk maps generated from incidence alone, our maps provide evidence that elimination programs must address ongoing movement of parasites around the lower transmission areas in the southwest.

epidemiology↗

Host contact dynamics shapes richness and dominance of pathogen strains

The interaction among multiple microbial strains affects the spread of infectious diseases and the efficacy of interventions. Genomic tools have made it increasingly easy to observe pathogenic strains diversity, but the best interpretation of such diversity has remained difficult because of relationships with host and environmental factors. Here, we focus on host-to-host contact behavior and study how it changes populations of pathogens in a minimal model of multi-strain interaction. We simulated a population of identical strains competing by mutual exclusion and spreading on a dynamical network of hosts according to a stochastic susceptible-infectious-susceptible model. We computed ecological indicators of diversity and dominance in strain populations for a collection of networks illustrating various properties found in real-world examples. Heterogeneities in the number of contacts among hosts were found to reduce diversity and increase dominance by making the repartition of strains among infected hosts more uneven, while strong community structure among hosts increased strain diversity. We found that the introduction of strains associated with hosts entering and leaving the system led to the highest pathogenic richness at intermediate turnover levels. These results were finally illustrated using the spread of Staphylococcus aureus in a long-term health-care facility where close proximity interactions and strain carriage were collected simultaneously. We found that network structural and temporal properties could account for a large part of the variability observed in strain diversity. These results show how stochasticity and network structure affect the population ecology of pathogens and warns against interpreting observations as unambiguous evidence of epidemiological differences between strains.\n\nAuthor summaryPathogens are structured in multiple strains that interact and co-circulate on the same host population. This ecological diversity affects, in many cases, the spread dynamics and the efficacy of vaccination and antibiotic treatment. Thus understanding its biological and host-behavioral drivers is crucial for outbreak assessment and for explaining trends of new-strain emergence. We used stochastic modeling and network theory to quantify the role of host contact behavior on strain richness and dominance. We systematically compared multi-strain spread on different network models displaying properties observed in real-world contact patterns. We then analyzed the real-case example of Staphylococcus aureus spread in a hospital, leveraging on a combined dataset of carriage and close proximity interactions. We found that contact dynamics has a profound impact on a strain population. Contact heterogeneity, for instance, reduces strain diversity by reducing the number of circulating strains and leading few strains to dominate over the others. These results have important implications in disease ecology and in the epidemiological interpretation of biological data.

epidemiology↗

Long-term coexistence and regional heterogeneity of antibiotic-resistant infections reproduced by a simple spatial model

Antibiotic-resistant infections are a growing threat to human health, but basic features of the eco-evolutionary dynamics remain unexplained. Most prominently, there is no clear mechanism for the long-term coexistence of both drug-sensitive and resistant strains at intermediate levels, a ubiquitous pattern seen in surveillance data. Here we show that accounting for structured or spatially-heterogeneous host populations and variability in antibiotic consumption can lead to persistent coexistence over a wide range of treatment coverages, drug efficacies, costs of resistance, and mixing patterns. Moreover, this mechanism can explain other puzzling spatiotemporal features of drug-resistance epidemiology that have received less attention, such as large differences in the prevalence of resistance between geographical regions with similar antibiotic consumption or that neighbor one another. We find that the same amount of antibiotic use can lead to very different levels of resistance depending on how treatment is distributed in a transmission network. We also identify parameter regimes in which population structure alone cannot support coexistence, suggesting the need for other mechanisms to explain the epidemiology of antibiotic resistance. Our analysis identifies key features of host population structure that can be used to assess resistance risk and highlights the need to include spatial or demographic heterogeneity in models to guide resistance management.

epidemiology↗

Automated image processing to support the analysis of between-year transmission of Leptosphaeria maculans in field conditions

Understanding the transmission of inoculum between periods where the host plants are present is central for predicting the development of plant diseases and optimising mitigation strategies. However, the production at the end of the growing period, the survival during the intercrop period, and the emergence or emission of inoculum after sowing or planting can be highly variable, difficult to assess and generally inferred indirectly from symptoms data. As a result, there is a lack of large data sets which is a major brake for the study of these epidemiological processes. Here we focus on Leptosphaeria maculans that causes the black leg of oilseed rape. After having infected leaves, at early stages of the plant, and migrating into the stem, it causes a basal stem canker before harvest. It then survives on stubble left in the field from which ascospores are emitted at the beginning of the next growing period. In this study we first developed an image processing framework to estimate the density of fruiting bodies produced on stubble. Then, we used this framework to analyse automatically a large number of stems collected in oilseed rape fields among a cultivated area. Having performed a quality assessment of the processing chain we used the output data to investigate how the potential level of inoculum may change with the source field, the considered year and the stem canker severity at harvest. Besides the insights gain into the blackleg of oilseed rape, this work shows how image-based phenotyping may support epidemiological studies by increasing substantially the precision of high throughput disease data.

epidemiology↗

Community-level impacts of spatial repellents for control of diseases vectored by Aedes aegypti mosquitoes

Spatial repellents (SRs) reduce human-mosquito contact by preventing mosquito entrance into human-occupied spaces and interfering with host-seeking and blood-feeding. A new model to synthesize experimental data on the effects of transfluthrin on Aedes aegypti explores how SR effects interact to impact the epidemiology of diseases vectored by these mosquitoes. Our results indicate that the greatest impact on force of infection is expected to derive from the chemicals lethal effect but delayed biting and associated negative feedbacks on the vector population could elicit substantial impact in the absence of lethality. The relative contributions of these effects depend on coverage, chemical dose, mechanism of action, and housing density. We also demonstrate potential adverse impacts of increased partial blood-feeding and reduced exiting, which could offset gains achieved by other effects. Our analysis demonstrates how small-scale experimental data can be leveraged to derive expectations of epidemiological impact of SRs deployed at larger scales.

epidemiology↗

Estimating prevalence for limb-girdle muscular dystrophy based on public sequencing databases

PurposeLimb Girdle Muscular Dystrophies (LGMD) are a genetically heterogeneous category of autosomal inherited muscle diseases. Many genes causing LGMD have been identified, and clinical trials are beginning for treatment of some genetic subtypes. However, even with the gene-level mechanisms known, it is still difficult to get a reliable and generalizable prevalence estimation for each subtype due to the limited amount of epidemiology data and the low incidence of LGMDs. MethodsTaking advantage of recently published whole exome and genome sequencing data from the general population, we used a Bayesian method to develop a reliable disease prevalence estimator. ResultsThis method was applied to nine recessive LGMD subtypes. The estimated disease prevalence calculated by this method were largely comparable to published estimates from epidemiological studies, however highlighted instances of possible under-diagnosis for LGMD2B and 2L. ConclusionThe increasing size of aggregated population variant databases will allow for robust and reproducible prevalence estimates of recessive disease, which is critical for the strategic design and prioritization of clinical trials.

epidemiology↗

Estimating the probable cause of recurrence in Plasmodium vivax malaria: relapse, reinfection or recrudescence?

BackgroundRelapse of Plasmodium vivax infection is the main cause of vivax malaria in many parts of Asia. However at the individual patient level, recurrence of a blood stage infection following treatment within the endemic area can be either a relapse (from dormant liver-stage parasites), a recrudescence (blood-stage treatment failure), or a reinfection (following a new mosquito inoculation). Each requires a different prevention strategy, but previously they could not be distinguished reliably. Time-of-event and genetic data provide complimentary information about the cause of P. vivax recurrence, but the optimum approach to genotyping and analysis remains uncertain.\n\nMethodsIndividual-level data from two large drug trials in acute vivax malaria patients (Vivax History: VHX; Best Primaquine Dose: BPD) conducted on the Thailand-Myanmar border with follow-up of one year were pooled (n=1299). A total of 710 isolates from both acute and recurrent P. vivax episodes were genotyped using 3-9 highly polymorphic microsatellite markers. These pooled data were analyzed using a novel population statistical model incorporating an assessment of genetic relatedness, treatment drug administered, and the time-to-recurrence.\n\nResults99% of genotyped recurrences in individuals who did not receive primaquine (n=365) were estimated to be relapses. In comparison, 14% of genotyped recurrences (n=121) were estimated to be relapses following high-dose supervised primaquine. By comparing episodes across individuals (90194 comparisons), the false-positive rate of relapse identification using genetic data alone was estimated to be 2.2%. We estimated the true failure rate after high-dose primaquine (7mg/kg total dose) to be 2.6% in this epidemiological context, substantially lower the reinfection unadjusted estimate of 12%. Simulation studies show that 9 highly polymorphic microsatellite markers suffice to discriminate between recurrence states. Drug exposures reflected by plasma carboxy-primaquine concentrations were not predictive of treatment failure, but did identify non-adherence.\n\nConclusionUsing this novel statistical model, relapse of P. vivax malaria could be distinguished reliably from reinfection. This showed that in this population supervised high-dose primaquine could avert up to 99% of relapses. In low transmission settings, microsatellite genotyping combined with time-to-event data can accurately discriminate between the different causes of recurrent P. vivax malaria.\n\nAuthor summaryOne hundred years ago, Plasmodium vivax, the most globally diverse cause of human malaria, was present across most of the old-world, throughout tropical and temperate climes. Its main evolutionary advantage over Plasmodium falciparum, responsible for the most deadly type of human malaria, is its ability to stay dormant in the liver, emerging weeks to years later causing recurring illness and continuing transmission. The dormant liver-stage parasites are called hypnozoites. A recurrent infection can either be hypnozoite-derived (a relapse), a blood-stage treatment failure (recrudescence), or from a new mosquito bite (a reinfection). At a population level, each requires a different preventative strategy but no widely applicable methodology existed to discriminate between the three possible states: relapse, recrudescence and reinfection. Parasite genetic data alone cannot resolve the different possibilities. We developed a novel probabilistic framework which uses both epidemiological and genetic data to determine the most likely cause of vivax recurrence at the individual level. We applied this method to the largest available pooled clinical trial data from Southeast Asia incorporating information from highly polymorphic microsatellite markers, drug treatments received and time-to-recurrent illness. This analysis provides a tool for estimating probabilities of relapse, recrudescence and reinfection and, importantly, shows that hypnozoiticidal high-dose primaquine radical cure is much more effective than previously thought.

epidemiology↗