Joint spatial time-series epidemiological analysis of malaria and cutaneous leishmaniasis infection

Abstract

Malaria and leishmaniasis are among the two most important health problems of many developing countries especially in the Middle East and North Africa. It is common for vector-borne infectious diseases to have similar hotspots which may be attributed to the overlapping ecological distribution of the vector. Hotspot analyses were conducted to simultaneously detect the location of local hotspots and test their statistical significance. Spatial scan statistics were used to detect and test hotspots of malaria and cutaneous leishmaniasis (CL) in Afghanistan in 2009. A multivariate negative binomial model was used to simultaneously assess the effects of environmental variables on malaria and CL. In addition to the dependency between malaria and CL disease counts, spatial and temporal information were also incorporated in the model. Results indicated that malaria and CL incidence peaked at the same periods. Two hotspots were detected for malaria and three for CL. The findings in the current study show an association between the incidence of malaria and CL in the studied areas of Afghanistan. The incidence of CL disease in a given month is linked with the incidence of malaria in the previous month. Co-existence of malaria and CL within the same geographical area was supported by this study, highlighting the presence and effects of environmental variables such as temperature and precipitation. People living in areas with malaria are at increased risk for leishmaniasis infection. Local healthcare authorities should consider the co-infection problem by recommending systematic malaria screening for all CL patients.

Spatial modelling of contribution of individual level risk factors for mortality from Middle East respiratory syndrome coronavirus in the Arabian Peninsula

“…SaTScan is widely used for local cluster detection, which is good for detecting large clusters as well as to evaluate outliers when the outlier pattern is very strong or a small maximum search window is used [ 28 ]. The idea of Poisson model based SaTScan circular version is to recognize sets of regions where the disease count is significantly larger than expected [ 29 ]. SaTScan’s Poisson log likelihood ratio statistics was applied to regional aggregated MERS counts in circular windows of increasing radius centered at each region centroid with a maximum cluster size of provinces covering 50% of the national population.…”

Spatial Modelling of Contribution of Individual Level Risk Factors for Mortality From Middle East Respiratory Syndrome Coronavirus in the Arabian Peninsula

“…SaTScan is widely used for local cluster detection, which is good for detecting large clusters as well as to evaluate outliers when the outlier pattern is very strong or a small maximum search window is used [28]. The idea of Poisson model based SaTScan circular version is to recognize sets of regions where the disease count is significantly larger than expected [29]. SaTScan's Poisson log likelihood ratio statistics was applied to regional aggregated MERS counts in circular windows of increasing radius centered at each region centroid with a maximum cluster size of provinces covering 50% of the national population.…”

“…This significance was assessed using the default 999 Monte Carlo trials drawn under the null hypothesis that the observed case count represents the census distribution. If the p-value derived by ranking a test statistic calculated from observed data against the 999 statistics calculated similarly for the Monte Carlo trials was below our alpha level of 5%, then the observed cluster was considered significant [27][28][29]. Additionally, the Wang's q-Statistics [30,31] was used to test the global stratified spatial heterogeneity of …”

The spatio-temporal epidemic dynamics of COVID-19 outbreak in Africa

“…There have been a number of applications of statistical models for prediction of infection rates and spread during the COVID-19 pandemic [9,10]. However, mapping of disease incidence to identify spatial clustering and patterns remains an important pathway to understanding disease epidemiology and is required for effective planning, prevention or containment action [11][12][13]. There are a few studies that attempt to map the pandemic in China [14] and in Iran [15].…”