Spatial cluster analysis of Plasmodium vivax and P. malariae exposure using serological data among Haitian school children sampled between 2014 and 2016

Background Estimation of malaria prevalence in very low transmission settings is difficult by even the most advanced diagnostic tests. Antibodies against malaria antigens provide an indicator of active or past exposure to these parasites. The prominent malaria species within Haiti is Plasmodium falciparum, but P. vivax and P. malariae infections are also known to be endemic. Methodology/Principal findings From 2014–2016, 28,681 Haitian children were enrolled in school-based serosurveys and were asked to provide a blood sample for detection of antibodies against multiple infectious diseases. IgG against the P. falciparum, P. vivax, and P. malariae merozoite surface protein 19kD subunit (MSP119) antigens was detected by a multiplex bead assay (MBA). A subset of samples was also tested for Plasmodium DNA by PCR assays, and for Plasmodium antigens by a multiplex antigen detection assay. Geospatial clustering of high seroprevalence areas for P. vivax and P. malariae antigens was assessed by both Ripley’s K-function and Kulldorff’s spatial scan statistic. Of 21,719 children enrolled in 680 schools in Haiti who provided samples to assay for IgG against PmMSP119, 278 (1.27%) were seropositive. Of 24,559 children enrolled in 788 schools providing samples for PvMSP119 serology, 113 (0.46%) were seropositive. Two significant clusters of seropositivity were identified throughout the country for P. malariae exposure, and two identified for P. vivax. No samples were found to be positive for Plasmodium DNA or antigens. Conclusions/Significance From school-based surveys conducted from 2014 to 2016, very few Haitian children had evidence of exposure to P. vivax or P. malariae, with no children testing positive for active infection. Spatial scan statistics identified non-overlapping areas of the country with higher seroprevalence for these two malarias. Serological data provides useful information of exposure to very low endemic malaria species in a population that is unlikely to present to clinics with symptomatic infections.

Spatiotemporal patterns of the COVID-19 epidemic in Mexico at the municipality level

In recent history, Coronavirus Disease 2019 (COVID-19) is one of the worst infectious disease outbreaks affecting humanity. The World Health Organization has defined the outbreak of COVID-19 as a pandemic, and the massive growth of the number of infected cases in a short time has caused enormous pressure on medical systems. Mexico surpassed 3.7 million confirmed infections and 285,000 deaths on October 23, 2021. We analysed the spatio-temporal patterns of the COVID-19 epidemic in Mexico using the georeferenced confirmed cases aggregated at the municipality level. We computed weekly Moran’s I index to assess spatial autocorrelation over time and identify clusters of the disease using the “flexibly shaped spatial scan” approach. Finally, we compared Euclidean, cost, resistance distances and gravitational model to select the best-suited approach to predict inter-municipality contagion. We found that COVID-19 pandemic in Mexico is characterised by clusters evolving in space and time as parallel epidemics. The gravitational distance was the best model to predict newly infected municipalities though the predictive power was relatively low and varied over time. This study helps us understand the spread of the epidemic over the Mexican territory and gives insights to model and predict the epidemic behaviour.

Geospatial inequality of anaemia among children in Ethiopia

Anaemia remains a severe public health problem among children in Ethiopia and targeted approaches, based on the distribution and specific risk factors for that setting are needed to efficiently target health interventions. An analysis was performed using Ethiopia Demographic and Health Survey 2016 data. Blood specimens for anaemia testing were collected from 9268 children aged 6-59 months. A child was considered as anaemic if the bloodhaemoglobin count was less than 11.0 g/dL. We applied Kulldorf’s spatial scan statistics and used SaTScanTM to identify locations and estimate cluster sizes. In addition, we ran the local indicator of spatial association and the Getis-Ord Gi* statistics to detect and locate hotspots and multilevel multivariable analysis to identify risk factors for anaemia clustering. More than half of children aged 6-59 months (57%) were found to be anaemic in Ethiopia. We found significant geospatial inequality of anaemia among children and identified clusters (hotspots) in the eastern part of Ethiopia. The odds of anaemia among children found within the identified cluster was 1.5 times higher than children found outside the cluster. Women anaemia, stunting and high fertility were associated with anaemia clustering.

Geographic distribution and prevalence of human echinococcosis at the township level in the Tibet Autonomous Region

Background Echinococcosis is a zoonotic parasitic disease caused by larval stages of cestodes belonging to the genus Echinococcus. Echinococcosis is prevalent in 10 provinces/autonomous regions in western and northern China. A 2016 epidemiological survey of Tibet Autonomous Region (TAR) showed that the prevalence of human echinococcosis was 1.66% which is much higher than the average prevalence in China (0.24%). Therefore, understanding the prevalence and spatial distribution characteristics of human echinococcosis at the township level in TAR is critical. Methods Data from echinococcosis cases were obtained from 692 TAR townships in 2018. Cases were identified using the B-ultrasonography diagnostic method. The epidemic status of echinococcosis was classified in all townships in TAR according to the relevant standards of population prevalence indexes as defined in the national technical plan for echinococcosis control. Spatial scan statistics were used to highlight the geographical townships most at risk of echinococcosis. SPSS 21.0 was used to calculate the prevalence for cystic echinococcosis (CE) and alveolar echinococcosis (AE). For spatial clustering analyses and mapping, data were processed using ArcGIS 10.1. Spatial scan analyses were performed using SaTScan V9.5. Results In 2018, 16,009 echinococcosis cases were recorded in 74 endemic counties in TAR. The total prevalence rate was 0.53%. All the 692 townships were classified according to the order of the epidemic degree from high to low. 127 townships had prevalence rates higher than or equal to 1%. The spatial clustering scanning analysis of echinococcosis cases and exposed population showed that CE displayed one primary cluster, two secondary clusters and six minor secondary clusters. The primary cluster and other clusters were defined by Log-likelihood ratio (LLR) statistically significant values. The primary cluster covered 88 townships in 12 epidemic counties. AE displayed one primary cluster and two secondary clusters. The primary cluster covered 38 townships in 6 epidemic counties.

Micro-Epidemiological Investigation of Echinococcus multilocularis in Wild Hosts from an Endemic Area of Southwestern Hungary

Echinococcus multilocularis is a tapeworm causing severe zoonotic disease in temperate Europe. Between 2018 and 2020, 68 golden jackals and 94 red foxes were investigated to determine the prevalence of E. multilocularis infection and its driving factors. The overall prevalence (golden jackal: 41.2%; red fox: 12.5%) significantly differed, whereas the mean intensities did not. The spatial scan statistics revealed three significant clusters of E. multilocularis infection. The binary logistic and ordinal regression results revealed that the golden jackal is more likely to become infected than the red fox, and the probability of infection level was also higher in jackals. Our findings highlight the golden jackal’s role, which could be as important as the red fox in the spread of this severe zoonotic agent. This micro-epidemiological approach can advance the knowledge on local drivers which facilitate the spread of E. multilocularis and could cause a relevant public health problem on the continent.

Epidemiological Factors Associated With Caligus rogercresseyi Infection, Abundance, and Spatial Distribution in Southern Chile

Sea lice (Caligus rogercresseyi) are external parasites that affect farmed salmonids in Chile, and the scale of their sanitary and economic impact cannot be overstated. Even though space–time patterns suppose parasite aggregation, specific locations related to different infestation levels, as well as their associated factors across the geographic range involved, had not been investigated as of the writing of the present article. The understanding of the effects and factors entailed by the presence of C. rogercresseyi may be deemed a key element of Integrated Pest Management (IPM). In the present study, the multivariate spatial scan statistic was used to identify geographic areas and times of C. rogercresseyi infestation and to estimate the factors associated with such patterns. We used official C. rogercresseyi monitoring data at the farm level, with a set of 13 covariates, to provide adjustment within the analyses. The analyses were carried out for a period of 5 years (2012–2016), and they included three fish species (Salmo salar, Oncorhynchus mykiss, and Oncorhynchus kisutch) in order to assess the consistency of the identified clusters. A retrospective multinomial, spatial, and temporal scan test was implemented to identify farm clusters of either of the different categories of C. rogercresseyi infested farms: baseline, medium, and high, based on the control chemical threshold established by the health authority. The baseline represents adequate farm performance against C. rogercresseyi infestation. Then, production and environmental factors of the medium and high infestation farms were compared with the baseline using regression techniques. The results revealed a total of 26 clusters (p < 0.001), of which 12 correspond to baseline, 1 to medium, and the remaining 13 to high infestation clusters. In general, baseline clusters are detected in a latitudinal gradient on estuarine areas, with increasing relative risks to complex island water systems. There is a spatial structure in specific sites, north of Los Lagos Region and central Aysén Region, with high infestation clusters and epidemic peaks during 2013. In addition, average weight, salmon species, chemotherapeutants, latitude, temperature, salinity, and year category are factors associated with these C. rogercresseyi patterns. Recommendations for an IPM plan are provided, along with a discussion that considers the involvement of stock density thresholds by salmon species and the spatial structure of the efficacy of chemical control, both intended to avoid the advance of resistance and to minimize environmental residues.

Optimizing the maximum reported cluster size in the spatial scan statistic for survival data

Background The spatial scan statistic is a useful tool for cluster detection analysis in geographical disease surveillance. The method requires users to specify the maximum scanning window size or the maximum reported cluster size (MRCS), which is often set to 50% of the total population. It is important to optimize the maximum reported cluster size, keeping the maximum scanning window size at as large as 50% of the total population, to obtain valid and meaningful results. Results We developed a measure, a Gini coefficient, to optimize the maximum reported cluster size for the exponential-based spatial scan statistic. The simulation study showed that the proposed method mostly selected the optimal MRCS, similar to the true cluster size. The detection accuracy was higher for the best chosen MRCS than at the default setting. The application of the method to the Korea Community Health Survey data supported that the proposed method can optimize the MRCS in spatial cluster detection analysis for survival data. Conclusions Using the Gini coefficient in the exponential-based spatial scan statistic can be very helpful for reporting more refined and informative clusters for survival data.