Dead bird clusters as an early warning system for West Nile virus activity

Cost-of-illness of gastroenteritis caused by rotavirus in Chinese children less than 5 years

Acute gastroenteritis (AGE) caused by rotavirus (RV) remains a public health issue in China. To accelerate the mass rotavirus vaccination, it is important to inform the policy maker, and the public of the economic burden caused by rotavirus infection. A meta-analysis was conducted applying standardized algorithms. Articles published before January 1, 2023, in English and Chinese were searched through PubMed, CNKI, and WanFang Data. Studies with cost analysis of RV AGE were included. A random-effects model was applied to synthesize the total cost of RV AGE from the societal perspective. A prospective survey aimed to measure the cost of RV AGE was conducted in 2021 and 2022 in Shaoxing city, Zhejiang province, that can represent the developed region. The cost data was applied as deviation indicator, in comparison with the pooled estimate generated from meta-analysis. Totally 286 articles were identified, and eventually 12 studies were included. The pooled total social cost of RV AGE was US$282.1 (95%CI: US$213.4-350.7). The pooled private cost of RV AGE was US$206.4 (95%CI: US$155.2-257.5). RV AGE hospitalized and RV AGE incurred in developed regions caused remarkable higher burden (US$631.2 [95%CI: US$512.6-749.8], and US$333.6 [95%CI: US$234.1-433.2] respectively), compared to RV AGE treated at outpatient, and incurred in less developed regions. Our study demonstrates that RV AGE causes a significant economic burden in China. Given the promising effectiveness and highly cost-effective, introduction of rotavirus vaccines in national immunization programs could substantially reduce the economic burden in China.

Epidemic versus endemic West Nile virus dead bird surveillance in California: Changes in sensitivity and focus

Since 2003, the California West Nile virus (WNV) dead bird surveillance program (DBSP) has monitored publicly reported dead birds for WNV surveillance and response. In the current paper, we compared DBSP data from early epidemic years (2004-2006) with recent endemic years (2018-2020), with a focus on specimen collection criteria, county report incidence, bird species selection, WNV prevalence in dead birds, and utility of the DBSP as an early environmental indicator of WNV. Although fewer agencies collected dead birds in recent years, most vector control agencies with consistent WNV activity continued to use dead birds as a surveillance tool, with streamlined operations enhancing efficiency. The number of dead bird reports was approximately ten times greater during 2004-2006 compared to 2018-2020, with reports from the Central Valley and portions of Southern California decreasing substantially in recent years; reports from the San Francisco Bay Area decreased less dramatically. Seven of ten counties with high numbers of dead bird reports were also high human WNV case burden areas. Dead corvid, sparrow, and quail reports decreased the most compared to other bird species reports. West Nile virus positive dead birds were the most frequent first indicators of WNV activity by county in 2004-2006, followed by positive mosquitoes; in contrast, during 2018-2020 mosquitoes were the most frequent first indicators followed by dead birds, and initial environmental WNV detections occurred later in the season during 2018-2020. Evidence for WNV impacts on avian populations and susceptibility are discussed. Although patterns of dead bird reports and WNV prevalence in tested dead birds have changed, dead birds have endured as a useful element within our multi-faceted WNV surveillance program.

Comprehensive Dynamic Influence of Multiple Meteorological Factors on the Detection Rate of Bacterial Foodborne Diseases under Spatio-Temporal Heterogeneity

Foodborne diseases are a critical public health problem worldwide and significantly impact human health, economic losses, and social dynamics. Understanding the dynamic relationship between the detection rate of bacterial foodborne diseases and a variety of meteorological factors is crucial for predicting outbreaks of bacterial foodborne diseases. This study analyzed the spatio-temporal patterns of vibriosis in Zhejiang Province from 2014 to 2018 at regional and weekly scales, investigating the dynamic effects of various meteorological factors. Vibriosis had a significant temporal and spatial pattern of aggregation, and a high incidence period occurred in the summer seasons from June to August. The detection rate of Vibrio parahaemolyticus in foodborne diseases was relatively high in the eastern coastal areas and northwestern Zhejiang Plain. Meteorological factors had lagging effects on the detection rate of V. parahaemolyticus (3 weeks for temperature, 8 weeks for relative humidity, 8 weeks for precipitation, and 2 weeks for sunlight hours), and the lag period varied in different spatial agglomeration regions. Therefore, disease control departments should launch vibriosis prevention and response programs that are two to eight weeks in advance of the current climate characteristics at different spatio-temporal clustering regions.

Criteria for selecting sentinel unit locations in a surveillance system for vector-borne disease: A decision tool

Objectives

With vector-borne diseases emerging across the globe, precipitated by climate change and other anthropogenic changes, it is critical for public health authorities to have well-designed surveillance strategies in place. Sentinel surveillance has been proposed as a cost-effective approach to surveillance in this context. However, spatial design of sentinel surveillance system has important impacts on surveillance outcomes, and careful selection of sentinel unit locations is therefore an essential component of planning.

Methods

A review of the available literature, based on the realist approach, was used to identify key decision issues for sentinel surveillance planning. Outcomes of the review were used to develop a decision tool, which was subsequently validated by experts in the field.

Results

The resulting decision tool provides a list of criteria which can be used to select sentinel unit locations. We illustrate its application using the case example of designing a national sentinel surveillance system for Lyme disease in Canada.

Conclusions

The decision tool provides researchers and public health authorities with a systematic, evidence-based approach for planning the spatial design of sentinel surveillance systems, taking into account the aims of the surveillance system and disease and/or context-specific considerations.

Presyndromic surveillance for improved detection of emerging public health threats

Existing public health surveillance systems that rely on predefined symptom categories, or syndromes, are effective at monitoring known illnesses, but there is a critical need for innovation in "presyndromic" surveillance that detects biothreats with rare or previously unseen symptomology. We introduce a data-driven, automated machine learning approach for presyndromic surveillance that learns newly emerging syndromes from free-text emergency department chief complaints, identifies localized case clusters among subpopulations, and incorporates practitioner feedback to automatically distinguish between relevant and irrelevant clusters, thus providing personalized, actionable decision support. Blinded evaluations by New York City's Department of Health and Mental Hygiene demonstrate that our approach identifies more events of public health interest and achieves a lower false-positive rate compared to a state-of-the-art baseline.

West Nile virus: another emerging arboviral risk for travelers?

Purpose of Review

West Nile virus (WNV) is an arbovirus transmitted by mosquitos of the genus Culex. Manifestations of WNV infection range from asymptomatic to devastating neuroinvasive disease leading to flaccid paralysis and death. This review examines WNV epidemiology and ecology, with an emphasis on travel-associated infection.

Recent Findings

WNV is widespread, including North America and Europe, where its range has expanded in the past decade. Rising temperatures in temperate regions are predicted to lead to an increased abundance of Culex mosquitoes and an increase in their ability to transmit WNV. Although the epidemiologic patterns of WNV appear variable, its geographic distribution most certainly will continue to increase. Travelers are at risk for WNV infection and its complications. Literature review identified 39 cases of documented travel-related WNV disease, the majority of which resulted in adverse outcomes, such as neuroinvasive disease, prolonged recovery period, or death.

Summary

The prediction of WNV risk is challenging due to the complex interactions of vector, pathogen, host, and environment. Travelers planning to visit endemic areas should be advised regarding WNV risk and mosquito bite prevention. Evaluation of ill travelers with compatible symptoms should consider the diagnosis of WNV for those visiting in endemic areas as well as for those returning from destinations with known WNV circulation.

Temporal and Spatial Patterns and a Space-Time Cluster Analysis of Foot-and-Mouth Disease Outbreaks in Ethiopia from 2010 to 2019

Foot-and-mouth disease (FMD) is an endemic disease in Ethiopia, although space-time cluster and monthly variation studies have never been assessed at national level. The current study aimed to identify the spatial and temporal distribution of FMD outbreaks in Ethiopia from national outbreak reports over a period of ten years from 1 January 2010 to 31 December 2019. To this end, a total of 376,762 cases and 1302 outbreaks from 704 districts were obtained from the Minister of Agriculture for analyses. In general, the dry periods, i.e., October to March, of the year were recorded as the peak outbreak periods, with the highest prevalence in March 2012. The monthly average and the outbreak trends over ten years show a decrease of outbreaks from 2010 to 2019. Decomposing the FMD outbreak data time series showed that once an outbreak erupted, it continued for up to five years. Only 12% of the reported outbreaks were assigned to a specific serotype. Within these outbreaks, the serotypes O, A, SAT-2, and SAT-1 were identified in decreasing order of prevalence, respectively. When a window of 50% for the maximum temporal/space cluster size was set, a total of seven FMD clusters were identified in space and time. The primary cluster with a radius of 380.95 km was identified in the southern part of Ethiopia, with a likelihood ratio of 7.67 (observed/expected cases). The third cluster, with a radius of 144.14 km, was identified in the northeastern part of the country, and had a likelihood ratio of 5.66. Clusters 1 and 3 occurred from January 2017 to December 2019. The second cluster that occurred had a radius of 294.82 km, a likelihood ratio of 6.20, and was located in the central and western parts of Ethiopia. The sixth cluster, with a radius of 36.04 km and a likelihood ratio of 20.60, was set in southern Tigray, bordering Afar. Clusters 2 and 6 occurred in the same period, from January 2014 to December 2016. The fourth cluster in northern Tigray had a calculated radius of 95.50 km and a likelihood ratio of 1.17. The seventh cluster occurred in the north-central Amhara region, with a radius of 97 km and a likelihood ratio of 1.16. Clusters 4 and 7 occurred between January 2010 and December 2013. The spatiotemporal and cluster analysis of the FMD outbreaks identified in the context of the current study are crucial in implementing control, prevention, and a prophylactic vaccination schedule. This study pointed out October to March as well as the main time of the year during which FMD outbreaks occur. The area that extends from the south to north, following the central highlands, is the main FMD outbreak area in Ethiopia.

How media presence triggers participation in citizen science-The case of the mosquito monitoring project 'Mückenatlas'

Since 2012, the citizen science project ‘Mückenatlas’ has been supplementing the German mosquito monitoring programme with over 28,000 submissions of physical insect samples. As the factors triggering people to catch mosquitoes for science are still unknown, we analysed the influence of mass media reports on mosquito submission numbers. Based on a theoretical framework of how mass media affect citizen responsiveness, we identified five possible influencing factors related to citizen science: (i) project awareness and knowledge, (ii) attention (economy), (iii) individual characteristics of citizen scientists and targeted communication, (iv) spatial differences and varying affectedness, and (v) media landscape. Hypotheses based on these influencing factors were quantitatively and qualitatively tested with two datasets: clipping data of mass media reports (online, television, radio and print) referring to or focussing on the ‘Mückenatlas’, and corresponding data of ‘Mückenatlas’ submissions between 2014 and 2017. In general, the number of media reports positively affected the number of mosquito submissions on a temporal and spatial scale, i.e. many media reports provoke many mosquito submissions. We found that an already heightened public and media awareness of mosquito-relevant topics combined with a direct call-to-action in a media report title led to a maximum participation. Differences on federal state level, however, suggest that factors additional to quantitative media coverage trigger participation in the ‘Mückenatlas’, in particular the mosquito affectedness of the resident population. Lastly, media types appear to differ in their effects on the number of submissions. Our results show under which circumstances the media presence of the ’Mückenatlas’ is most effective in activating people to submit mosquito samples, and thus provide advice for designing communication strategies for citizen science projects.

Development of an Index to Measure West Nile Virus Transmission Risk in New Jersey Counties

We developed an index for use by New Jersey counties to measure West Nile virus (WNV) transmission risk to the human population. We used a latent profile analysis to develop the index, identifying categories of environmental conditions associated with WNV transmission risk to humans. The final model included 4 indicators of transmission risk: mosquito abundance and minimum field infection rate, temperature, and human case count. We used data from 2004 to 2018 from all 21 New Jersey counties aggregated into 11 2-wk units per county per year (N = 3,465). Three WNV risk classes were identified. The Low Risk class had low levels of all variables. The Moderate Risk class had high abundance, average temperature levels, and low levels of the other variables. The High Risk class had substantially above average human case likelihood, average temperature, and high mosquito infection rates. These results suggest the presence of 3 distinct WNV risk profiles, which can be used to guide the development of public health actions intended to mitigate WNV transmission risk to the human population.

Spatiotemporal Analysis of West Nile Virus Epidemic in South Banat District, Serbia, 2017-2019

West Nile virus (WNV) is an arthropod-born pathogen, which is transmitted from wild birds through mosquitoes to humans and animals. At the end of the 20th century, the first West Nile fever (WNF) outbreaks among humans in urban environments in Eastern Europe and the United States were reported. The disease continued to spread to other parts of the continents. In Serbia, the largest number of WNV-infected people was recorded in 2018. This research used spatial statistics to identify clusters of WNV infection in humans and animals in South Banat County, Serbia. The occurrence of WNV infection and risk factors were analyzed using a negative binomial regression model. Our research indicated that climatic factors were the main determinant of WNV distribution and were predictors of endemicity. Precipitation and water levels of rivers had an important influence on mosquito abundance and affected the habitats of wild birds, which are important for maintaining the virus in nature. We found that the maximum temperature of the warmest part of the year and the annual temperature range; and hydrographic variables, e.g., the presence of rivers and water streams were the best environmental predictors of WNF outbreaks in South Banat County.

Geographic Core Areas of Coinfections in Washington, District of Columbia: Recommendations for Planning Prevention-Intervention to Mitigate Human Immunodeficiency Virus Burden

BACKGROUND

Research suggests that human immunodeficiency virus (HIV)-positive individuals with a sexually transmitted infection (STI) may be at increased risk of transmitting HIV to someone else through unprotected sex. The primary aim of the analysis is to identify the high-risk geographic areas of transmission of coinfections and factors that may be associated with poor outcomes of viral suppression within these higher-risk geographic areas, thus important in transmission prevention.

METHODS

We used surveillance data reported by all providers and laboratories in the District of Columbia (DC). Applied discrete Poisson scan model in SaTScan to identify the geographic areas. The relative risk (RR) for the scan statistic was calculated based on events inside the cluster, and P values evaluated statistical significance. We used multinomial logistical regression to explore care and demographical characteristics associated with being virally unsuppressed within and outside the geographic areas.

RESULTS

The coinfected areas (RR, >1; P < .001) were located in the tracts of central and southern DC. Black population (RR, 3.154 [95% confidence interval {CI}, 1.736-5.729]), age 13-19 years (RR, 4.598 [95% CI, 3.176-6.657]), repeat STIs (RR, 1.387 [95% CI, 1.096-1.754]), and not retained in care (RR, 2.546 [95% CI, 1.997-3.245]) were found to be at higher risk of being virally unsuppressed within the coinfected clusters. Those with unknown linkages were found to be at higher risk of being virally unsuppressed outside the coinfected clusters (RR, 5.162 [95% CI, 2.289-11.640]).

CONCLUSIONS

This is DC's first effort to identify the geographic core areas of coinfections and factors that may be sustaining them. These results will be used by the health department to plan for prevention-intervention strategies. This model be replicated by any local jurisdiction similar.

Detecting Spatial Cores and Temporal Trends of Repeat STIs to Plan Pre-exposure Prophylaxis (PrEP) Scale-up in DC

BACKGROUND

Repeat sexually transmitted infections (STIs) in DC primarily results from untreated sexual partners. This analysis aims to identify high-risk areas and temporal trends of repeat STIs for pre-exposure prophylaxis scale-up and STI mitigation in DC.

METHODS

We identified repeat infections in the DC Department of Health STI and HIV data management systems, diagnosed from 2014 to 2018. The cases were geocoded and aggregated by census tracts. Poisson discrete scan statistic was implemented in SaTScan software to find clusters. Weighted moving average was used to compare temporal trends of repeat STIs. We used χ analysis to identify association with demographic variables.

RESULTS

We identified 8535 repeat STIs from 2014 to 2018. Of these, 61.84% were among men, most cases were among blacks (34.75%) and 47.45% represented gonorrhea cases. The high-risk spatial clusters were identified as those tracts that had relative risk (relative risk > 1; P-value < 0.001). We identified one significant radius of risk covering tracts of wards 7 and 8 and parts of wards 5 and 6. We spotted positive temporal trends in cluster 1 and outside the cluster. We found significant associations of repeat STIs with gender (χ = 317.27, P < 0.001), age (χ = 539.26, P < 0.001), HIV coinfections (χ = 352.06, P < 0.001), and year of diagnoses (χ = 1.5, P < 0.01).

CONCLUSIONS

Our findings indicate spatial disparities in DC for repeat STIs. This analysis is critical for pre-exposure prophylaxis planning, STI prevention strategies such as expedited partner therapies and condom distribution strategies in DC should prioritize the high-risk spatial cores.

Spatial-temporal clusters of host-seeking Aedes albopictus, Aedes japonicus, and Aedes triseriatus collections in a La Crosse virus endemic county (Knox County, Tennessee, USA)

A bite from a La Crosse virus (LACV) infected Aedes mosquito can cause La Crosse encephalitis (LACE), which is a neuro-invasive disease that disproportionately affects children under the age of 16 in Southern Appalachia. The three vectors for LACV are Aedes albopictus (Skuse), Ae. japonicus (Theobald), and Ae. triseriatus (Say). Localized maps of the geographic distribution of vectors are practical tools for mosquito management personnel to target areas with high mosquito abundance. This study hypothesized that LACV vectors have unique species-specific spatial and temporal clusters. To test this, 44 sites were identified in Knox County, Tennessee for their land use/type. At each site, host-seeking mosquitoes were collected approximately every other week from May-October 2018. Spatial clusters of host-seeking mosquito collections for each of the three mosquito species were investigated using Kulldorff's spatial scan statistic, specifying a retrospective space-time Bernoulli model. Most vector clusters were identified in south-central Knox County while the seasonality of clusters varied by mosquito species. Clusters of Ae. albopictus were observed throughout the entire study period while clusters of Ae. japonicus and Ae. triseriatus only occurred May-June. The findings indicate that the relative abundance of LACV vectors were more abundant in south-central Knox County compared to the rest of the county. Of interest, these clusters spatially overlapped with previous LACE diagnosed cases. These findings are useful in guiding decisions on targeted mosquito control in Knox County and may be applied to other counties within Southern Appalachia.

Exploring the socio-economic and environmental components of infectious diseases using multivariate geovisualization: West Nile Virus

Background

This study postulates that underlying environmental conditions and a susceptible population's socio-economic status should be explored simultaneously to adequately understand a vector borne disease infection risk. Here we focus on West Nile Virus (WNV), a mosquito borne pathogen, as a case study for spatial data visualization of environmental characteristics of a vector's habitat alongside human demographic composition for understanding potential public health risks of infectious disease. Multiple efforts have attempted to predict WNV environmental risk, while others have documented factors related to human vulnerability to the disease. However, analytical modeling that combines the two is difficult due to the number of potential explanatory variables, varying spatial resolutions of available data, and differing research questions that drove the initial data collection. We propose that the use of geovisualization may provide a glimpse into the large number of potential variables influencing the disease and help distill them into a smaller number that might reveal hidden and unknown patterns. This geovisual look at the data might then guide development of analytical models that can combine environmental and socio-economic data.

Methods

Geovisualization was used to integrate an environmental model of the disease vector's habitat alongside human risk factors derived from socio-economic variables. County level WNV incidence rates from California, USA, were used to define a geographically constrained study area where environmental and socio-economic data were extracted from 1,133 census tracts. A previously developed mosquito habitat model that was significantly related to WNV infected dead birds was used to describe the environmental components of the study area. Self-organizing maps found 49 clusters, each of which contained census tracts that were more similar to each other in terms of WNV environmental and socio-economic data. Parallel coordinate plots permitted visualization of each cluster's data, uncovering patterns that allowed final census tract mapping exposing complex spatial patterns contained within the clusters.

Results

Our results suggest that simultaneously visualizing environmental and socio-economic data supports a fuller understanding of the underlying spatial processes for risks to vector-borne disease. Unexpected patterns were revealed in our study that would be useful for developing future multilevel analytical models. For example, when the cluster that contained census tracts with the highest median age was examined, it was determined that those census tracts only contained moderate mosquito habitat risk. Likewise, the cluster that contained census tracts with the highest mosquito habitat risk had populations with moderate median age. Finally, the cluster that contained census tracts with the highest WNV human incidence rates had unexpectedly low mosquito habitat risk.

Temporal patterns and space-time cluster analysis of foot-and-mouth disease (FMD) cases from 2007 to 2017 in Vietnam

In Vietnam, Foot‐and‐mouth disease (FMD) is endemic, but no nationwide studies have been conducted to assess the monthly variations and space‐time clusters of FMD. The main objective was to identify the temporal patterns and space‐time clusters of FMD from 2007 to 2017 using national surveillance data in Vietnam. A total of 163,733 cases were reported from 2007 to 2017. Among them, the proportion of buffaloes (43.31% of total reported cases; 70,909 cases) was highest followed by cattle (30.11%; 49,306 cases), pigs (26.67%; 43,662 cases) and sheep/goats (0.41%; 675 cases). The serotype O was widely distributed across the country while serotype A was observed in Northeast, Central and Southern part of Vietnam while Asia 1 has been not identified since 2007. For monthly variations, most cases were observed during the dry season (from November to March) except Central Highlands. Under the spatial window was set at 50%, a total of seven clusters were identified. The primary cluster was observed from Dec 2009 to Dec 2010 in the northwest (radius: 101.67 km), showing a ratio of 3.75. The secondary cluster was detected in the northeast region (radius: 76.54 km) with a ratio of 3.53 in Feb 2017. The 3rd cluster was the largest with a radius of 176.69 km and located in the southern part of Vietnam. Interestingly, the most temporal clusters included between December and March during the study period. Our findings provide better insight into the temporal patterns and distribution of clusters of FMD in Vietnam. This study provides useful information to policymakers on the hotspot areas and timing of outbreaks. It also identifies when and where national surveillance and control programmes could be implemented more efficiently for the prevention and control of FMD.

Where did I get dengue? Detecting spatial clusters of infection risk with social network data

Typical spatial disease surveillance systems associate a single address to each disease case reported, usually the residence address. Social network data offers a unique opportunity to obtain information on the spatial movements of individuals as well as their disease status as cases or controls. This provides information to identify visit locations with high risk of infection, even in regions where no one lives such as parks and entertainment zones. We develop two probability models to characterize the high-risk regions. We use a large Twitter dataset from Brazilian users to search for spatial clusters through analysis of the tweets' locations and textual content. We apply our models to both real-world and simulated data, demonstrating the advantage of our models as compared to the usual spatial scan statistic for this type of data.

Simulation of Legionnaires' disease prospective spatiotemporal cluster detection, Allegheny County, Pennsylvania, USA

Legionnaires’ disease (LD) incidence in the USA has quadrupled since 2000. Health departments must detect LD outbreaks quickly to identify and remediate sources. We tested the performance of a system to prospectively detect simulated LD outbreaks in Allegheny County, Pennsylvania, USA. We generated three simulated LD outbreaks based on published outbreaks. After verifying no significant clusters existed in surveillance data during 2014–2016, we embedded simulated outbreak-associated cases into 2016, assigning simulated residences and report dates. We mimicked daily analyses in 2016 using the prospective space-time permutation scan statistic to detect clusters of ⩽30 and ⩽180 days using 365-day and 730-day baseline periods, respectively. We used recurrence interval (RI) thresholds of ⩾20, ⩾100 and ⩾365 days to define significant signals. We calculated sensitivity, specificity and positive and negative predictive values for daily analyses, separately for each embedded outbreak. Two large, simulated cooling tower-associated outbreaks were detected. As the RI threshold was increased, sensitivity and negative predictive value decreased, while positive predictive value and specificity increased. A small, simulated potable water-associated outbreak was not detected. Use of a RI threshold of ⩾100 days minimised time-to-detection while maximizing positive predictive value. Health departments should consider using this system to detect community-acquired LD outbreaks.

Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization

Syndromic Surveillance aims at analyzing medical data to detect clusters of illness or forecast disease outbreaks. Although the research in this field is flourishing in terms of publications, an insight of the global research output has been overlooked. This paper aims at analyzing the global scientific output of the research from 1993 to 2017. To this end, the paper uses bibliometric analysis and visualization to achieve its goal. Particularly, a data processing framework was proposed based on citation datasets collected from Scopus and Clarivate Analytics’ Web of Science Core Collection (WoSCC). The bibliometric method and Citespace were used to analyze the institutions, countries, and research areas as well as the current hotspots and trends. The preprocessed dataset includes 14,680 citation records. The analysis uncovered USA, England, Canada, France and Australia as the top five most productive countries publishing about Syndromic Surveillance. On the other hand, at the Pinnacle of academic institutions are the US Centers for Disease Control and Prevention (CDC). The reference co-citation analysis uncovered the common research venues and further analysis of the keyword cooccurrence revealed the most trending topics. The findings of this research will help in enriching the field with a comprehensive view of the status and future trends of the research on Syndromic Surveillance.

Detecting spatial clusters of HIV and hepatitis coinfections

BACKGROUND

People with HIV infection in the United States are often affected by chronic viral hepatitis. These coinfected people with either HBV or HCV are at increased risk for serious, life-threatening complications. Coinfections with viral hepatitis may also complicate the delivery of anti-retroviral therapy (ART) by escalating the risk of drug-related hepatoxicity. According to the Centers for Disease Control and Prevention (CDC), approximately 10 percent of people with HIV in the United States also have HBV, and 25 percent also have HCV coinfection. With the advent of highly active antiretroviral therapy (HAART) and the increased life-expectancy of HIV patients, clinicians are more likely to be confronted with issues related to co-infection and the management challenges that they present, especially in resource-limited settings. The purpose of this analysis was to identify geographical clusters of HIV- (HBV/HCV) co-infection and compared to the geographical clusters of not co-infected using DC, Department of Health surveillance data. The results of the analysis will be used to target resources to areas at risk.

METHODS

HIV and Hepatitis surveillance data were matched among cases diagnosed between 1980 and 2016. HIV-hepatitis co-infected and the not co-infected spatial clusters were detected using discrete Poisson model. Kulldorff's spatial scan statistic method was implemented in the free software tool called SaTScan which has been widely adopted for detecting disease cluster. The analysis was conducted by tracts, but for visualization, ease of interpretation and assist in policy making the tract map was overlaid with the ward map using ArcGIS 10.5.1.

RESULTS

Between 1980 and 2016, there were 12,965 diagnosed cases of HIV, of which 2,316 HIV/Hepatitis matches were identified. Of the 2316 co-infected people living in DC, 25 percent (N = 590) of people had HBV, and 75 percent (N = 1,726) had HCV. Out of 12,965 diagnosed cases, remaining 10,649 did not have any co-infections (not co-infected). IDU (27.16 percent) and MSM (32.86 percent) were the highest mode of transmission for co-infected population. African-American were reported 83.64 percent (N = 1,937) among co-infection population. Three clusters were identified for both co-infected population in DC. The largest cluster radius for co-infected analysis covers wards 6, 7 and 8 as well as large parts of 2 and 5 (p < 0.001). Multiple clusters were identified for not co-infected population (p < 0.001). IDU (n = 450) was the highest mode of transmission for the co-infected clusters. For all clusters combined of not co-infected population highest mode of transmission were MSM (n = 2,534). This analysis also showed racial disparity, economic deprivation and lack of education were prominent in the co-infected clusters.

CONCLUSION

We identified locations of high risk clusters where enhanced hepatitis and HIV prevention, treatment, and care can help combat the epidemic. The clusters radius expands into the neighboring state of Maryland as well. The findings from this analysis will be used to target area based public health policy and healthcare interventions for HIV-hepatitis. It is recommended based on the analysis that needle exchange programs can successfully control new HIV infections as well as hepatitis co-infections.

Effective surveillance systems for vector-borne diseases in urban settings and translation of the data into action: a scoping review

Background

Vector-borne diseases (VBDs) continue to represent a global threat, with “old” diseases like malaria, and “emergent” or “re-emergent” ones like Zika, because of an increase in international trade, demographic growth, and rapid urbanization. In this era of globalization, surveillance is a key element in controlling VBDs in urban settings, but surveillance alone cannot solve the problem. A review of experiences is of interest to examine other solution elements. The objectives were to assess the different means of VBD surveillance in urban environments, to evaluate their potential for supporting public health actions, and to describe the tools used for public health actions, the constraints they face, and the research and health action gaps to be filled.

Main body

For this scoping review we searched peer-reviewed articles and grey literature published between 2000 and 2016. Various tools were used for data coding and extraction. A quality assessment was done for each study reviewed, and descriptive characteristics and data on implementation process and transferability were analyzed in all studies.

After screening 414 full-text articles, we retained a total of 79 articles for review. The main targets of the articles were arboviral diseases (65.8%) and malaria (16.5%). The positive aspects of many studies fit within the framework of integrated vector management. Public awareness is considered a key to successful vector control programs. Advocacy and legislation can reinforce both empowerment and capacity building. These can be achieved by collaboration within the health sector and with other sectors. Research is needed to develop well designed studies and new tools for surveillance and control.

Conclusions

The need for surveillance systems in urban settings in both developing and developed countries was highlighted. Countries face the same challenges relating to human, financial, and structural resources. These findings also constitute a wake-up call for governments, academia, funders, and World Health Organization to strengthen control programs and enhance VBD research in urban environments.

Electronic supplementary material

The online version of this article (10.1186/s40249-018-0473-9) contains supplementary material, which is available to authorized users.

Overwintering of West Nile virus in a bird community with a communal crow roost

In temperate climates, transmission of West Nile virus (WNV) is detectable rarely during the coldest months (late fall through early spring), yet the virus has reappeared consistently during the next warm season. Several mechanisms may contribute to WNV persistence through winter, including bird-to-bird transmission among highly viremic species. Here we consider whether, under realistic scenarios supported by field and laboratory evidence, a winter bird community could sustain WNV through the winter in the absence of mosquitoes. With this purpose we constructed a deterministic model for a community of susceptible birds consisting of communally roosting crows, raptors and other birds. We simulated WNV introduction and subsequent transmission dynamics during the winter under realistic initial conditions and model parameterizations, including plausible contact rates for roosting crows. Model results were used to determine whether the bird community could yield realistic outbreaks that would result in WNV infectious individuals at the end of the winter, which would set up the potential for onward horizontal transmission into summer. Our findings strongly suggest that winter crow roosts could allow for WNV persistence through the winter, and our model results provide synthesis to explain inconclusive results from field studies on WNV overwintering in crow roosts.

Comparative Vector Competence of North American Culex pipiens and Culex quinquefasciatus for African and European Lineage 2 West Nile Viruses

West Nile virus (WNV) is a mosquito-borne flavivirus that is phylogenetically separated into distinct lineages. Lineage 1 (L1) and lineage 2 (L2) encompass all WNV isolates associated with human and veterinary disease cases. Although L1 WNV is globally distributed, including North America, L2 WNV only recently emerged out of sub-Saharan Africa into Europe and Russia. The spread of L2 WNV throughout and beyond Europe depends, in part, on availability of competent vectors. The vector competence of mosquitoes within the Culex genus for WNV is well established for L1 WNV but less extensively studied for L2 WNV. Assessing the vector competence of North American Culex mosquitoes for L2 WNV will be critical for predicting the potential for L2 WNV emergence in North America. We address the vector competence of North American Culex pipiens and Culex quinquefasciatus for L2 WNV. Both mosquito species were highly competent for each of the L2 WNV strains assessed, but variation in infection, dissemination, and transmission was observed. An L2 WNV strain (NS10) isolated during the Greek outbreak in 2010 exhibited a reduced capacity to infect Cx. pipiens compared with other L2 WNV strains. In addition, a South African L2 WNV strain (SA89) displayed a significantly shorter extrinsic incubation period in Cx. quinquefasciatus compared with other L2 WNV strains. These results demonstrate that North American Culex mosquito species are competent vectors of African and European L2 WNV and that emergence of L2 WNV is unlikely to be hindered by poor competence of North American vectors.

Waterborne disease outbreak detection: an integrated approach using health administrative databases

Hundreds of waterborne disease outbreaks (WBDO) of acute gastroenteritis (AGI) due to contaminated tap water are reported in developed countries each year. Such outbreaks are probably under-detected. The aim of our study was to develop an integrated approach to detect and study clusters of AGI in geographical areas with homogeneous exposure to drinking water. Data for the number of AGI cases are available at the municipality level while exposure to tap water depends on drinking water networks (DWN). These two geographical units do not systematically overlap. This study proposed to develop an algorithm which would match the most relevant grouping of municipalities with a specific DWN, in order that tap water exposure can be taken into account when investigating future disease outbreaks. A space-time detection method was applied to the grouping of municipalities. Seven hundred and fourteen new geographical areas (groupings of municipalities) were obtained compared with the 1,310 municipalities and the 1,706 DWN. Eleven potential WBDO were identified in these groupings of municipalities. For ten of them, additional environmental investigations identified at least one event that could have caused microbiological contamination of DWN in the days previous to the occurrence of a reported WBDO.

A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters

BACKGROUND

The primary aim of the study reported here was to determine the effectiveness of utilizing local spatial variations in environmental data to uncover the statistical relationships between West Nile Virus (WNV) risk and environmental factors. Because least squares regression methods do not account for spatial autocorrelation and non-stationarity of the type of spatial data analyzed for studies that explore the relationship between WNV and environmental determinants, we hypothesized that a geographically weighted regression model would help us better understand how environmental factors are related to WNV risk patterns without the confounding effects of spatial non-stationarity.

METHODS

We examined commonly mapped environmental factors using both ordinary least squares regression (LSR) and geographically weighted regression (GWR). Both types of models were applied to examine the relationship between WNV-infected dead bird counts and various environmental factors for those locations. The goal was to determine which approach yielded a better predictive model.

RESULTS

LSR efforts lead to identifying three environmental variables that were statistically significantly related to WNV infected dead birds (adjusted R2 = 0.61): stream density, road density, and land surface temperature. GWR efforts increased the explanatory value of these three environmental variables with better spatial precision (adjusted R2 = 0.71).

CONCLUSIONS

The spatial granularity resulting from the geographically weighted approach provides a better understanding of how environmental spatial heterogeneity is related to WNV risk as implied by WNV infected dead birds, which should allow improved planning of public health management strategies.

Early detection of West Nile virus in France: quantitative assessment of syndromic surveillance system using nervous signs in horses

West Nile virus (WNV) is a growing public health concern in Europe and there is a need to develop more efficient early detection systems. Nervous signs in horses are considered to be an early indicator of WNV and, using them in a syndromic surveillance system, might be relevant. In our study, we assessed whether or not data collected by the passive French surveillance system for the surveillance of equine diseases can be used routinely for the detection of WNV. We tested several pre-processing methods and detection algorithms based on regression. We evaluated system performances using simulated and authentic data and compared them to those of the surveillance system currently in place. Our results show that the current detection algorithm provided similar performances to those tested using simulated and real data. However, regression models can be easily and better adapted to surveillance objectives. The detection performances obtained were compatible with the early detection of WNV outbreaks in France (i.e. sensitivity 98%, specificity >94%, timeliness 2·5 weeks and around four false alarms per year) but further work is needed to determine the most suitable alarm threshold for WNV surveillance in France using cost-efficiency analysis.

Faster Detection of Poliomyelitis Outbreaks to Support Polio Eradication

As the global eradication of poliomyelitis approaches the final stages, prompt detection of new outbreaks is critical to enable a fast and effective outbreak response. Surveillance relies on reporting of acute flaccid paralysis (AFP) cases and laboratory confirmation through isolation of poliovirus from stool. However, delayed sample collection and testing can delay outbreak detection. We investigated whether weekly testing for clusters of AFP by location and time, using the Kulldorff scan statistic, could provide an early warning for outbreaks in 20 countries. A mixed-effects regression model was used to predict background rates of nonpolio AFP at the district level. In Tajikistan and Congo, testing for AFP clusters would have resulted in an outbreak warning 39 and 11 days, respectively, before official confirmation of large outbreaks. This method has relatively high specificity and could be integrated into the current polio information system to support rapid outbreak response activities.

Assessing surveillance using sensitivity, specificity and timeliness

Monitoring ongoing processes of illness to detect sudden changes is an important aspect of practical epidemiology and medicine more generally. Most commonly, the monitoring has been restricted to a unidimensional stream of data over time. In such situations, analytic results from the industrial process monitoring have suggested optimal approaches to monitor the data streams. Data streams including spatial location as well as temporal sequence are becoming available. Monitoring methods that incorporate spatial data may prove superior to those that ignore it. However, analytically, optimal methods for spatial surveil-lance data may not exist. In the present article, we introduce and discuss evaluation metrics that can be used to compare the performance of statistical methods of surveillance. Our general approach is to generalize receiver operating characteristic (ROC) curves to incorporate the time of detection in addition to the usual test characteristics of sensitivity and specificity. In addition to weighting ordinary ROC curves by two measures of timeliness, we describe three three-dimensional generalizations of ROC curves that result in timeliness-ROC surfaces. Working in the context of surveillance of cases of disease to detect a sudden outbreak, we demonstrate these in an artificial example and in a previously described simulation context and show how the metrics differ. We also discuss the differences and under which circumstances one might prefer a given method.

Evaluation of the Control of West Nile Virus in Ontario: Did Risk Patterns Change from 2005 to 2012?

The goal of this study was to evaluate and compare the risk distribution of human cases of West Nile virus (WNV) disease in Ontario in 2005 to 2012. The objectives were to: map the risk distribution of WNV in 2005 and 2012, identify clusters of human WNV disease and determine whether the clusters are significantly different between the years 2005 and 2012. West Nile virus surveillance data were used to calculate empirical Bayesian smoothed estimates of disease incidence in southern Ontario for 2005 and 2012. Choropleth maps were generated to visualize the spatial risk distribution, and the spatial scan test was performed to identify clusters of disease. Following identification of clusters for 2005 and 2012, a Poisson model was applied to the 2012 human WNV incidence adjusted for the smoothed human WNV incidence rate from 2005 and the scan test was repeated. Two significant clusters were identified in both the year 2005 and 2012. In 2005, the primary cluster was located in the Windsor-Essex and Chatham-Kent public health units (PHUs). For 2012, the primary cluster was identified in the Golden Horseshoe area. A cluster analysis for 2012 adjusted for those identified in 2005 resulted in one significant cluster in the Windsor-Essex PHU. In 2012, the Windsor-Essex PHU remained as a high-risk area for human WNV disease when compared with the rest of southern Ontario. Although overall risk may change from year to year, public health programming should be employed to decrease the relative risk of WNV in this area.

Evaluation of Antigen-Capture ELISA and Immunohistochemical Methods for Avian Surveillance of West Nile Virus

Accurate detection of West Nile virus (WNV) in corvids is essential for monitoring the spread of virus during the mosquito season. Viremia in corvids is very high, with titers approaching 108 viral particles/ml. In the presence of such marked viremia, the sensitivity of real-time reverse transcription-polymerase chain reaction (RT-PCR) analysis is unnecessary, and more cost-effective methods should be assessed. To this end, antigen-capture ELISA (ACE) and immunohistochemical (IHC) assays were evaluated. Skin, cloacal swab specimens, and feathers from corvids were tested by use of ACE, and results were compared with results obtained from use of real-time RT-PCR analysis. Of the 3 sample types, skin gave the best sensitivity (98%) and specificity (100%). Skin, brain, kidney, and spleen from corvids were analyzed by IHC, and results were compared with real-time RT-PCR results. Kidney and spleen were more often positive by use of IHC than were brain and skin tissue; however, IHC did not perform as well as ACE in the identification of virus-positive birds. Results of this study support the use of a skin sample in an ACE format as an effective surveillance method for corvids.

Evaluation of a Multivariate Syndromic Surveillance System for West Nile Virus

BACKGROUND

Various methods are currently used for the early detection of West Nile virus (WNV) but their outputs are not quantitative and/or do not take into account all available information. Our study aimed to test a multivariate syndromic surveillance system to evaluate if the sensitivity and the specificity of detection of WNV could be improved.

METHODS

Weekly time series data on nervous syndromes in horses and mortality in both horses and wild birds were used. Baselines were fitted to the three time series and used to simulate 100 years of surveillance data. WNV outbreaks were simulated and inserted into the baselines based on historical data and expert opinion. Univariate and multivariate syndromic surveillance systems were tested to gauge how well they detected the outbreaks; detection was based on an empirical Bayesian approach. The systems' performances were compared using measures of sensitivity, specificity, and area under receiver operating characteristic curve (AUC).

RESULTS

When data sources were considered separately (i.e., univariate systems), the best detection performance was obtained using the data set of nervous symptoms in horses compared to those of bird and horse mortality (AUCs equal to 0.80, 0.75, and 0.50, respectively). A multivariate outbreak detection system that used nervous symptoms in horses and bird mortality generated the best performance (AUC = 0.87).

CONCLUSIONS

The proposed approach is suitable for performing multivariate syndromic surveillance of WNV outbreaks. This is particularly relevant, given that a multivariate surveillance system performed better than a univariate approach. Such a surveillance system could be especially useful in serving as an alert for the possibility of human viral infections. This approach can be also used for other diseases for which multiple sources of evidence are available.

Comparison of the efficiency and cost of West Nile virus surveillance methods in California

Surveillance systems for West Nile virus (WNV) combine several methods to determine the location and timing of viral amplification. The value of each surveillance method must be measured against its efficiency and costs to optimize integrated vector management and suppress WNV transmission to the human population. Here we extend previous comparisons of WNV surveillance methods by equitably comparing the most common methods after standardization on the basis of spatial sampling density and costs, and by estimating optimal levels of sampling effort for mosquito traps and sentinel chicken flocks. In general, testing for evidence of viral RNA in mosquitoes and public-reported dead birds resulted in detection of WNV approximately 2-5 weeks earlier than serological monitoring of sentinel chickens at equal spatial sampling density. For a fixed cost, testing of dead birds reported by the public was found to be the most cost effective of the methods, yielding the highest number of positive results per $1000. Increased spatial density of mosquito trapping was associated with more precise estimates of WNV infection prevalence in mosquitoes. Our findings also suggested that the most common chicken flock size of 10 birds could be reduced to six to seven without substantial reductions in timeliness or sensitivity. We conclude that a surveillance system that uses the testing of dead birds reported by the public complemented by strategically timed mosquito and chicken sampling as agency resources allow would detect viral activity efficiently in terms of effort and costs, so long as susceptible bird species that experience a high mortality rate from infection with WNV, such as corvids, are present in the area.

Spatiotemporal Co-occurrence of Flanders and West Nile Viruses Within Culex Populations in Shelby County, Tennessee

West Nile virus (WNV) and Flanders virus (FLAV) can cocirculate in Culex mosquitoes in parts of North America. A large dataset of mosquito pools tested for WNV and FLAV was queried to understand the spatiotemporal relationship between these two viruses in Shelby County, TN. We found strong evidence of global clustering (i.e., spatial autocorrelation) and overlapping of local clustering (i.e., Hot Spots based on Getis Ord Gi*) of maximum likelihood estimates (MLE) of infection rates (IR) during 2008-2013. Temporally, FLAV emerges and peaks on average 10.2 wk prior to WNV based on IR. Higher levels of WNV IR were detected within 3,000 m of FLAV-positive pool buffers than outside these buffers.

Will integrated surveillance systems for vectors and vector-borne diseases be the future of controlling vector-borne diseases? A practical example from China

Vector-borne diseases are one of the world's major public health threats and annually responsible for 30-50% of deaths reported to the national notifiable disease system in China. To control vector-borne diseases, a unified, effective and economic surveillance system is urgently needed; all of the current surveillance systems in China waste resources and/or information. Here, we review some current surveillance systems and present a concept for an integrated surveillance system combining existing vector and vector-borne disease monitoring systems. The integrated surveillance system has been tested in pilot programmes in China and led to a 21·6% cost saving in rodent-borne disease surveillance. We share some experiences gained from these programmes.

Economic Assessment of Zoonoses Surveillance in a 'One Health' Context: A Conceptual Framework

Collaboration between animal and public health sectors has been highlighted as a means to improve the management of zoonotic threats. This includes surveillance systems for zoonoses, where enhanced cross-sectoral integration and sharing of information are seen as key to improved public health outcomes. Yet, there is a lack of evidence on the economic returns of such collaboration, particularly in the development and implementation of surveillance programmes. The economic assessment of surveillance in this context needs to be underpinned by the understanding of the links between zoonotic disease surveillance in animal populations and the wider public health disease mitigation process and how these relations impact on the costs and benefits of the surveillance activities. This study presents a conceptual framework of these links as a basis for the economic assessment of cross-sectoral zoonoses surveillance with the aim of supporting the prioritization of resource allocation to surveillance. In the proposed framework, monetary, non-monetary and intermediate or intangible cost components and benefit streams of three conceptually distinct stages of zoonotic disease mitigation are identified. In each stage, as the final disease mitigation objective varies so does the use of surveillance information generated in the animal populations for public health decision-making. Consequently, the associated cost components and benefit streams also change. Building on the proposed framework and taking into account these links, practical steps for its application are presented and future challenges are discussed.

Satellite Hyperspectral Imagery to Support Tick-Borne Infectious Diseases Surveillance

This study proposed the use of satellite hyperspectral imagery to support tick-borne infectious diseases surveillance based on monitoring the variation in amplifier hosts food sources. To verify this strategy, we used the data of the human rickettsiosis occurrences in southeastern Brazil, region in which the emergence of this disease is associated with the rising capybara population. Spatio-temporal analysis based on Monte Carlo simulations was used to identify risk areas of human rickettsiosis and hyperspectral moderate-resolution imagery was used to identify the increment and expansion of sugarcane crops, main food source of capybaras. In general, a pixel abundance associated with increment of sugarcane crops was detected in risk areas of human rickettsiosis. Thus, the hypothesis that there is a spatio-temporal relationship between the occurrence of human rickettsiosis and the sugarcane crops increment was verified. Therefore, due to the difficulty of monitoring locally the distribution of infectious agents, vectors and animal host's, satellite hyperspectral imagery can be used as a complementary tool for the surveillance of tick-borne infectious diseases and potentially of other vector-borne diseases.

WEST NILE VIRUS-RELATED TRENDS IN AVIAN MORTALITY IN CALIFORNIA, USA, 2003-12

West Nile virus (WNV) is an arbovirus transmitted enzootically by Culex mosquitoes among avian hosts. Since 2000, the California Dead Bird Surveillance Program (DBSP) has tracked avian mortality reported by the public on a telephone hotline and website and measured the prevalence of WNV infection in dead birds. We summarize herein WNV prevalence in dead birds tested and variation of WNV transmission over time and space with the use of DBSP data from 2003 to 2012. Prevalence among dead birds was highest in 2004, 2008, and 2012. This pattern was similar to peak WNV infection years for mosquitoes but not to human WNV incidence. Although American Crows (Corvus brachyrhynchos) were most frequently reported and tested, this species ranked third in infection prevalence (44%) after Yellow-billed Magpies (Pica nuttalli; 62%) and Western Scrub-Jays (Aphelocoma californica; 48%). Overall prevalence in American Robin (Turdus migratorius), House Finch (Haemorhous mexicanus), and House Sparrow (Passer domesticus) carcasses ranged from 18% to 22%. Corvid WNV prevalence was highest in South Coast, Bay/Delta, Sacramento, and San Joaquin valleys, and Klamath/North Coast bioregions, overlapping areas of elevated WNV activity in other surveillance measurements. Bioregional analysis revealed the avian species most likely to be reported and found positive in each bioregion. Our results may be useful to WNV surveillance and control efforts and provide insight into bird population trends in California.

Early detection of emerging zoonotic diseases with animal morbidity and mortality monitoring

Diseases transmitted between animals and people have made up more than 50% of emerging infectious diseases in humans over the last 60 years and have continued to arise in recent months. Yet, public health and animal disease surveillance programs continue to operate independently. Here, we assessed whether recent emerging zoonotic pathogens (n = 143) are known to cause morbidity or mortality in their animal host and if so, whether they were first detected with an animal morbidity/mortality event. We show that although sick or dead animals are often associated with these pathogens (52%), only 9% were first detected from an animal morbidity or mortality event prior to or concurrent with signs of illness in humans. We propose that an animal morbidity and mortality reporting program will improve detection and should be an essential component of early warning systems for zoonotic diseases. With the use of widespread low-cost technology, such a program could engage both the public and professionals and be easily tested and further incorporated as part of surveillance efforts by public health officials.

Application of Data Mining Techniques to Healthcare Data

A high-level introduction to data mining as it relates to surveillance of healthcare data is presented. Data mining is compared with traditional statistics, some advantages of automated data systems are identified, and some data mining strategies and algorithms are described. A concrete example illustrates steps involved in the data mining process, and three successful data mining applications in the healthcare arena are described.

Systematic review of surveillance systems and methods for early detection of exotic, new and re-emerging diseases in animal populations

In this globalized world, the spread of new, exotic and re-emerging diseases has become one of the most important threats to animal production and public health. This systematic review analyses conventional and novel early detection methods applied to surveillance. In all, 125 scientific documents were considered for this study. Exotic (n = 49) and re-emerging (n = 27) diseases constituted the most frequently represented health threats. In addition, the majority of studies were related to zoonoses (n = 66). The approaches found in the review could be divided in surveillance modalities, both active (n = 23) and passive (n = 5); and tools and methodologies that support surveillance activities (n = 57). Combinations of surveillance modalities and tools (n = 40) were also found. Risk-based approaches were very common (n = 60), especially in the papers describing tools and methodologies (n = 50). The main applications, benefits and limitations of each approach were extracted from the papers. This information will be very useful for informing the development of tools to facilitate the design of cost-effective surveillance strategies. Thus, the current literature review provides key information about the advantages, disadvantages, limitations and potential application of methodologies for the early detection of new, exotic and re-emerging diseases.

West Nile virus state of the art report of MALWEST Project

During the last three years Greece is experiencing the emergence of West Nile virus (WNV) epidemics. Within this framework, an integrated surveillance and control programme (MALWEST project) with thirteen associate partners was launched aiming to investigate the disease and suggest appropriate interventions. One out of seven work packages of the project is dedicated to the State of the Art report for WNV. Three expert working groups on humans, animals and mosquitoes were established. Medical databases (PubMed, Scopus) were searched together with websites: e.g., WHO, CDC, ECDC. In total, 1,092 relevant articles were initially identified and 258 of them were finally included as references regarding the current knowledge about WNV, along with 36 additional sources (conference papers, reports, book chapters). The review is divided in three sections according to the fields of interest: (1) WNV in humans (epidemiology, molecular characteristics, transmission, diagnosis, treatment, prevention, surveillance); (2) WNV in animals (epidemiological and transmission characteristics concerning birds, horses, reptiles and other animal species) and (3) WNV in mosquitoes (control, surveillance). Finally, some examples of integrated surveillance programmes are presented. The introduction and establishment of the disease in Greece and other European countries further emphasizes the need for thorough research and broadening of our knowledge on this viral pathogen.

Spatio-temporal epidemiology of human West Nile virus disease in South Dakota

Despite a cold temperate climate and low human population density, the Northern Great Plains has become a persistent hot spot for human West Nile virus (WNV) disease in North America. Understanding the spatial and temporal patterns of WNV can provide insights into the epidemiological and ecological factors that influence disease emergence and persistence. We analyzed the 1,962 cases of human WNV disease that occurred in South Dakota from 2002-2012 to identify the geographic distribution, seasonal cycles, and interannual variability of disease risk. The geographic and seasonal patterns of WNV have changed since the invasion and initial epidemic in 2002-2003, with cases shifting toward the eastern portion of South Dakota and occurring earlier in the transmission season in more recent years. WNV cases were temporally autocorrelated at lags of up to six weeks and early season cumulative case numbers were correlated with seasonal totals, indicating the possibility of using these data for short-term early detection of outbreaks. Epidemiological data are likely to be most effective for early warning of WNV virus outbreaks if they are integrated with entomological surveillance and environmental monitoring to leverage the strengths and minimize the weaknesses of each information source.

Exploring the spatio-temporal dynamics of reservoir hosts, vectors, and human hosts of West Nile virus: a review of the recent literature

Over the last two decades West Nile Virus (WNV) has been responsible for significant disease outbreaks in humans and animals in many parts of the World. Its extremely rapid global diffusion argues for a better understanding of its geographic extent. The purpose of this inquiry was to explore spatio-temporal patterns of WNV using geospatial technologies to study populations of the reservoir hosts, vectors, and human hosts, in addition to the spatio-temporal interactions among these populations. Review of the recent literature on spatial WNV disease risk modeling led to the conclusion that numerous environmental factors might be critical for its dissemination. New Geographic Information Systems (GIS)-based studies are monitoring occurrence at the macro-level, and helping pinpoint areas of occurrence at the micro-level, where geographically-targeted, species-specific control measures are sometimes taken and more sophisticated methods of surveillance have been used.

Spatio-temporal analysis of Salmonella surveillance data in Thailand

This study evaluates the usefulness of spatio-temporal statistical tools to detect outbreaks using routine surveillance data where limited epidemiological information is available. A dataset from 2002 to 2007 containing information regarding date, origin, source and serotype of 29,586 Salmonella isolates from Thailand was analysed. Data was grouped into human and non-human categories and the analysis was performed for the top five occurring serovars for each year of the study period. A total 91 human and 39 non-human significant spatio-temporal clusters were observed, accounting for 11% and 16% of the isolates, respectively. Serovar-specific associations between human and non-human clusters were also evaluated. Results show that these statistical tools can provide information for use in outbreak prevention and detection, in countries where only limited data is available. Moreover, it is suggested that monitoring non-human reservoirs can be relevant in predicting future Salmonella human cases.

West Nile virus infection rates and avian serology in east-central Illinois

Understanding the geographic role of different species of mosquito vectors and vertebrate hosts in West Nile virus (WNV) transmission cycles can facilitate the development and implementation of targeted surveillance and control measures. This study examined the relationship between WNV-antibody rates in birds and mosquito infection rates and bloodfeeding patterns in east-central Illinois. The earliest detection of WNV-RNA by reverse transcription-polymerase chain reaction TaqMan was from Culex restuans; however, amplification typically coincided with an increase in abundance of Cx. pipiens. Trap type influenced annual estimates of infection rates in Culex species, as well as estimation of blood meal source. Bird species with the highest WNV-antibody rates (i.e., Mourning Doves [Zenaida macroura], Northern Cardinals [Cardinalis cardinalis], American Robins [Turdus migratorius], and House Sparrows [Passer domesticus]) were also the common species found in Culex blood meals. Although antibody rates were not directly proportional to estimated avian abundance, the apparent availability of mammal species did influence proportion of mammal to bird blood meals. Antibody prevalence in the American Robin was lower than expected based on the strong attraction of Culex to American Robins for blood meals. Age-related differences in serology were evident, antibody rates increased in older groups of robins and sparrows, whereas 1st-year hatch and older adults of Mourning Doves and Northern Cardinals had equally high rates of antibody-positive serum samples. The vector and host interactions observed in east-central Illinois (Champaign County), an urban area surrounded by agriculture, are compared to studies in the densely population areas of southern Cook County.

Epidemiological perspectives on West Nile virus surveillance in wild birds in Great Britain

West Nile virus (WNV) is a zoonotic arthropod-borne pathogen with continued geographical expansion in Europe. We present and evaluate data on the temporal, spatial and bird species focus of the WNV surveillance programme in dead wild birds in Great Britain (2002-2009). During this period all bird samples tested negative for WNV. Eighty-two per cent of the 2072 submissions occurred during the peak period of vector activity with 53% tested during April-July before human and equine infection would be expected. Samples were received from every county, but there was significant geographical clustering (nearest neighbour index=0·23, P<0·001). Over 240 species were represented, with surveillance more likely to detect WNV in resident bird species (92% of submissions) than migrants (8%). Evidence indicates that widespread avian mortality is not generally a reported feature of WNV in Europe and hence additional activities other than dead bird surveillance may maximize the ability to detect WNV circulation before the onset of human and equine infections.

Spatial patterns of malaria reported deaths in Yunnan Province, China

Malaria has been a heavy social and health burden in the remote and poor areas in southern China. Analyses of malaria epidemic patterns can uncover important features of malaria transmission. This study identified spatial clusters, seasonal patterns, and geographic variations of malaria deaths at a county level in Yunnan, China, during 1991-2010. A discrete Poisson model was used to identify purely spatial clusters of malaria deaths. Logistic regression analysis was performed to detect changes in geographic patterns. The results show that malaria mortality had declined in Yunnan over the study period and the most likely spatial clusters (relative risk [RR] = 23.03-32.06, P < 0.001) of malaria deaths were identified in western Yunnan along the China-Myanmar border. The highest risk of malaria deaths occurred in autumn (RR = 58.91, P < 0.001) and summer (RR = 31.91, P < 0.001). The results suggested that the geographic distribution of malaria deaths was significantly changed with longitude, which indicated there was decreased mortality of malaria in eastern areas over the last two decades, although there was no significant change in latitude during the same period. Public health interventions should target populations in western Yunnan along border areas, especially focusing on floating populations crossing international borders.