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bioRxiv2017

Stability in the genetic structure of a Zymoseptoria tritici population from epidemic to interepidemic stages at a small spatial scale

Subpopulations of the wheat pathogen Zymoseptoria tritici (26 sample groups composed of 794 strains) were collected in two nearby wheat fields in the Paris basin, during both epidemic and inter-epidemic periods of three successive years (2009-2013). In addition to the type of inoculum (ascospores vs. pycnidiospores), the alternative presence of wheat debris allowed taking into account its putative origin (local vs. distant). We used a molecular epidemiology approach, based on population genetic indices derived from SSR marker analysis, to describe putative changes in the structure and genotypic diversity of these subpopulations over three years, at a spatiotemporal scale consistent with epidemiological observations. Genetic structure was stable over time (within and between years) and between fields. All subpopulations displayed very high levels of gene and genotypic diversity. The low levels of linkage disequilibrium and the very low clonal fraction at all stages were consistent with the regular occurrence of sexual reproduction in the two fields. A significant increase of the MAT1-1/MAT1-2 ratio was observed over the course of the epidemics, suggesting a competitive advantage of MAT1-1 strains consistently with their greater pathogenicity reported in the literature. Finally, we found that the period, the type of inoculum and its putative origin had little effect on the short term evolution of the local population of Z. tritici. Fungal population size and diversity are apparently large enough to prevent genetic drift at this fine spatiotemporal scale, and more likely short distance migration contributes strongly to the stabilization of genetic diversity among and within plots.

epidemiology↗

bioRxiv2017

Transmission patterns of hyper-endemic multi-drug resistant Klebsiella pneumoniae in a Cambodian neonatal unit: a longitudinal study with whole genome sequencing

BackgroundKlebsiella pneumoniae is an important and increasing cause of life-threatening disease in hospitalised neonates. Third generation cephalosporin resistance (3GC-R) is frequently a marker of multi-drug resistance, and can complicate management of infections. 3GC-R K. pneumoniae is hyper-endemic in many developing country settings, but its epidemiology is poorly understood and prospective studies of endemic transmission are lacking. We aimed to determine the transmission dynamics of 3GC-R K. pneumoniae in a newly opened neonatal unit (NU) in Cambodia.\n\nMethodsWe performed a prospective longitudinal study between September and November 2013. Rectal swabs from 37 consented patients were collected upon NU admission and every three days thereafter. Morphologically different colonies from swabs growing cefpodoxime-resistant K. pneumoniae were selected for whole-genome sequencing (WGS).\n\nResults32/37 (86%) patients screened positive for 3GC-R K. pneumoniae and 93 colonies from 119 swabs were sequenced. Isolates were resistant to a median of six (range 3-9) antimicrobials. WGS revealed high diversity; pairwise distances between isolates from the same patient were either 0-1 SNV or >1,000 SNVs; 19/32 colonized patients harboured K. pneumoniae colonies differing by >1000 SNVs. Diverse lineages accounted for 18 probable importations to the NU and nine probable transmission clusters involving 19/37 (51%) of screened patients. Median cluster size was 5 patients (range 3-9).\n\nConclusionsThe epidemiology of 3GC-R K. pneumoniae was characterised by multiple introductions and a dense network of cross-infection, with half of screened neonates part of a transmission cluster. Efforts to reduce the 3GC-R K. pneumoniae disease burden should consider targeting both processes.

epidemiology↗

bioRxiv2017

Excess deaths associated with the chikungunya epidemic of 2014 in Jamaica were higher among children under 5 and over 40 years of age, an analysis based on official data.

We assessed the excess of all causes of mortality by age groups during the chikungunya epidemics in Jamaica, 2014. Excess mortality was estimated by subtracting deaths observed in 2014 from that expected based on the average mortality rate of 2012-2013, with confidence interval of 99%.\n\nOverall mortality 91.9 / 100,000 population, 2,499 additional deaths than expected coincided with the peak of the epidemic, there was a strong correlation between the monthly incidence and the excess of deaths (Spearman Rho = 0.939; p <0.005). No other significant epidemiological phenomenon occurred on that island that could explain this increase in mortality. Thus, we suggest that mortality associated with chikungunya is underestimated in Jamaica, as in other countries.\n\nThe excess of deaths could be a strategic tool for the epidemiological surveillance of chikungunya as it has already been used in influenza and respiratory syncytial.

epidemiology↗

bioRxiv2017

Excess mortality in Guadeloupe and Martinique, islands of the French West Indies, during the chikungunya epidemic of 2014

In some chikugunya epidemics, deaths are not fully captured by the traditional surveillance system, based on case reports and death reports. This is a time series study to evaluate the excess of mortality associated with epidemic of chikungunya virus (CHIKV) in Guadeloupe and Martinique, Antilles, 2014. The population (total 784,097 inhabitants) and mortality data estimated by sex and age were accessed at the Institut National de la Statistique et des Etudes Economiques - France. Age adjusted mortality rates were calculated also in Reunion, Indian Ocean for comparison. Epidemiological data on CHIKV (cases, hospitalizations, and deaths) were obtained in the official epidemiological reports of the Cellule de Institut de Veille Sanitaire - France. The excess of deaths for each month in 2014 and 2015 was the difference between the expected and observed deaths for all age groups, considering the 99% confidence interval threshold. Pearson coefficient of correlation between monthly excess of deaths and reported cases of chikungunya show a strong correlation (R = 0.81, p <0.005), also with a 1-month lag (R = 0.87, p <0.001), and between monthly rates of hospitalization for CHIKV and the excess of deaths with a delay of 1 month (R = 0.87, p <0.0005).The peak of the epidemic occurred in the month with the highest mortality, returning to normal soon after the end of the CHIKV epidemic. The overall mortality estimated by this method (639 deaths) was about 4 times greater than that obtained through death declarations (160 deaths). Excess mortality increased with age. Although etiological diagnosis of all deaths associated with CHIKV infection is not possible, already well-known statistical tools can contribute to an evaluation of the impact of this virus on the mortality and morbidity in the different age groups.

epidemiology↗

bioRxiv2017

Measuring mosquito-borne viral suitability and its implications for Zika virus transmission in Myanmar

INTRODUCTION: In South East Asia, mosquito-borne viruses (MBVs) have long been a cause of high disease burden and significant economic costs. While in some SEA countries the epidemiology of MBVs is spatio-temporally well characterised and understood, in others such as Myanmar our understanding is largely incomplete. MATERIALS AND METHODS: Here, we use a simple mathematical approach to estimate a climate-driven suitability index aiming to better characterise the intrinsic, spatio-temporal potential of MBVs in Myanmar. RESULTS: Results show that the timing and amplitude of the natural oscillations of our suitability index are highly informative for the temporal patterns of DENV case counts at the country level, and a mosquito-abundance measure at a city level. When projected at fine spatial scales, the suitability index suggests that the time period of highest MBV transmission potential is between June and October independently of geographical location. Higher potential is nonetheless found along the middle axis of the country and in particular in the southern corridor of international borders with Thailand. DISCUSSION: This research complements and expands our current understanding of MBV transmission potential in Myanmar, by identifying key spatial heterogeneities and temporal windows of importance for surveillance and control. We discuss our findings in the context of Zika virus given its recent worldwide emergence, public health impact, and current lack of information on its epidemiology and transmission potential in Myanmar. The proposed suitability index here demonstrated is applicable to other regions of the world for which surveillance data is missing, either due to lack of resources or absence of an MBV of interest.

epidemiology↗

bioRxiv2018

Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease

Prognostic modelling is important in clinical practice and epidemiology for patient management and research. Electronic health records (EHR) provide large quantities of data for such models, but conventional epidemiological approaches require significant researcher time to implement. Expert selection of variables, fine-tuning of variable transformations and interactions, and imputing missing values in datasets are time-consuming and could bias subsequent analysis, particularly given that missingness in EHR is both high, and may carry meaning.\n\nUsing a cohort of over 80,000 patients from the CALIBER programme, we performed a systematic comparison of several machine-learning approaches in EHR. We used Cox models and random survival forests with and without imputation on 27 expert-selected variables to predict all-cause mortality. We also used Cox models, random forests and elastic net regression on an extended dataset with 586 variables to build prognostic models and identify novel prognostic factors without prior expert input.\n\nWe observed that data-driven models used on an extended dataset can outperform conventional models for prognosis, without data preprocessing or imputing missing values, and with no need to scale or transform continuous data. An elastic net Cox regression based with 586 unimputed variables with continuous values discretised achieved a C-index of 0.801 (bootstrapped 95% CI 0.799 to 0.802), compared to 0.793 (0.791 to 0.794) for a traditional Cox model comprising 27 expert-selected variables with imputation for missing values.\n\nWe also found that data-driven models allow identification of novel prognostic variables; that the absence of values for particular variables carries meaning, and can have significant implications for prognosis; and that variables often have a nonlinear association with mortality, which discretised Cox models and random forests can elucidate.\n\nThis demonstrates that machine-learning approaches applied to raw EHR data can be used to build reliable models for use in research and clinical practice, and identify novel predictive variables and their effects to inform future research.

epidemiology↗

bioRxiv2018

Downgrading disease transmission risk estimates using terminal importations

As emerging and re-emerging infectious diseases like dengue, Ebola, chikungunya, and Zika threaten new populations worldwide, officials scramble to assess local severity and transmissibility, with little to no epidemiological history to draw upon. Standard methods for assessing autochthonous (local) transmission risk make either indirect estimates based on ecological suitability or direct estimates only after local cases accumulate. However, an overlooked source of epidemiological data that can meaningfully inform risk assessments prior to outbreak emergence is the absence of transmission by imported cases. Here, we present a method for updating a priori ecological estimates of transmission risk using real-time importation data. We demonstrate our method using Zika importation and transmission data from Texas in 2016, a high-risk region in the southern United States. Our updated risk estimates are lower than previously reported, with only six counties in Texas likely to sustain a Zika epidemic, and consistent with the number of autochthonous cases detected in 2017. Importation events can thereby provide critical, early insight into local transmission risks as infectious diseases expand their global reach.

epidemiology↗

bioRxiv2018

The Parkinson’s Phenome: Traits Associated with Parkinson’s Disease in a Large and Deeply Phenotyped Cohort

BackgroundObservational studies have begun to characterize the wide spectrum of phenotypes associated with Parkinsons disease (PD), but recruiting large numbers of PD cases and assaying a diversity of phenotypes has often been difficult. Here, we set out to systematically describe the PD phenome using a cross-sectional case-control design in a large database.\n\nMethodsWe analyzed the association between PD and 840 phenotypes derived from online surveys. For each phenotype, we ran a logistic regression using an average of 5,141 PD cases and 65,459 age- and sex-matched controls. We selected uncorrelated phenotypes, determined statistical significance after correcting for multiple testing, and systematically assessed the novelty of each significant association. We tested whether significant phenotypes were also associated with disease duration in PD cases.\n\nFindingsPD diagnosis was associated with 149 independent phenotypes. We replicated 32 known associations and discovered 49 associations that have not previously been reported. We found that migraine, obsessive-compulsive disorder, seasonal allergies, and anemia were associated with PD, but were not significantly associated with PD duration, and tend to occur decades before the average age of diagnosis for PD. Further work is needed to determine whether these phenotypes are PD risk factors or whether they share common disease mechanisms.\n\nInterpretationWe used a systematic approach in a single large dataset to assess the spectrum of traits that were associated with PD. Some of these traits may be risk factors for PD, features of the pre-diagnostic phase of disease, or manifestations of PD pathology. The model outputs from all 840 logistic regressions are available to the research community and may be used to generate hypotheses regarding PD etiology.\n\nFundingThe Michael J. Fox Foundation, Parkinsons UK, Barts Charity, National Institute on Aging, and 23andMe, Inc.\n\nResearch in ContextO_ST_ABSEvidence before this studyC_ST_ABSWe used PubMed to perform a MEDLINE database search for review articles published up to January 21st, 2018 that contained the keywords \"Parkinson\" and \"epidemiology\" in the title or abstract. We performed additional MEDLINE searches for each phenotype that was significantly associated with PD. Although dozens of phenotypes have been tested for an association with PD, only a few associations have been consistently repeatable (e.g. pesticide exposure, coffee consumption).\n\nAdded value of this studyWe systematically tested for an association between PD and 840 phenotypes using up to 13,546 cases and 1{middle dot}3 million controls, making this one of the largest PD epidemiology studies ever conducted. We discovered 49 novel associations that will need to be replicated or validated. We found 44 associations for phenotypes that have previously been studied in relation to PD, but for which an association has not been consistently demonstrated.\n\nImplications of all the available evidenceTaken together with results from previous studies, this series of case-control analyses adds evidence for associations between PD and many phenotypes that are not currently thought to be part of the canonical PD phenome. This work paves the way for future studies to assess whether any of these phenotypes represent PD risk factors and whether any of these risk factors are modifiable.

epidemiology↗

bioRxiv2018

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↗

bioRxiv2018

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↗

bioRxiv2018

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↗

bioRxiv2018

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↗

bioRxiv2018

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↗

bioRxiv2018

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↗

bioRxiv2018

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↗

bioRxiv2018

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↗

bioRxiv2018

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↗

bioRxiv2018

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↗