Space-time clustering characteristics of dengue based on ecological, socio-economic and demographic factors in northern Sri Lanka

Abstract

The aim of the present study was to identify geographical areas and time periods of potential clusters of dengue cases based on ecological, socio-economic and demographic factors in northern Sri Lanka from January 2010 to December 2013. Remote sensing (RS) was used to develop an index comprising rainfall, humidity and temperature data. Remote sensing data gathered by the AVNIR-2 instrument onboard the ALOS satellite were used to detect urbanisation, and a digital land cover map was used to extract land cover information. Other data on relevant factors and dengue outbreaks were collected through institutions and extant databases. The analysed RS data and databases were integrated into a geographical information system (GIS) enabling space-time clustering analysis. Our results indicate that increases in the number of combinations of ecological, socio-economic and demographic factors that are present or above the average contribute to significantly high rates of space-time dengue clusters. The spatio-temporal association that consolidates the two kinds of associations into one can ensure a more stable model for forecasting. An integrated spatiotemporal prediction model at a smaller level using ecological, socioeconomic and demographic factors could lead to substantial improvements in dengue control and prevention by allocating the right resources to the appropriate places at the right time.

Space Time Clusters of Dengue Fever in Medan Municipality, North Sumatera, Indonesia

“…It showed the significant spatiotemporal variation pattern of DF in the study area. Our finding is in line with other studies of space time scan statistic of DF carried out by Dhewantara et al in Indonesia (12) , Anno et al in Sri Lanka (13) , Chuang et al in Taiwan (20) , Acharya et al in Nepal (21) , and Hamer et al in Suriname (22) confirmed that DF cases tend to cluster.…”

“…Therefore, these approaches are useful for prioritizing DF surveillance and vector control. (12,13) Therefore, it is needed to discover the distribution pattern of DF cases and determine the spread of the disease spatially in Medan Municipality. This study aimed to investigate the presence of DF clusters in Medan Municipality and the temporal distribution pattern of detected such cluster over the four years.…”

Spatial-temporal distribution of dengue and climate characteristics for two clusters in Sri Lanka from 2012 to 2016

“…For example, Sri Lanka reported an infection rate of per people, and approximately a quarter of notified cases occurred in children under years old living in dengue transmission areas 16 . And since the early 2000s, progressively larger epidemics in Sri Lanka have occurred every regular period of time and increased in magnitude and frequency 17 . In urban areas, the disease incidence is the highest, especially in the district of Colombo, the most densely populated part of the country 18 .…”

Applications of Space Technologies to Global Health: Scoping Review

“…Applications of spatial technologies for malaria transmission modeling and control were reviewed in 2015 by Gebreslasie [ 48 ]. In addition to malaria [ 8 - 10 , 17 , 24 , 26 , 31 , 33 - 35 , 38 - 42 , 46 , 48 , 49 , 55 , 56 , 76 , 87 , 90 - 93 , 99 , 100 , 102 , 103 , 105 , 111 - 115 , 118 , 119 , 122 , 129 - 132 , 141 ] and schistosomiasis [ 15 , 37 , 53 , 54 , 63 , 95 , 124 , 126 , 127 , 142 - 145 , 153 , 154 , 156 , 158 ], dengue fever [ 7 , 12 , 13 , 16 , 23 , 44 , 89 , 94 , 98 , 101 , 117 , 140 ], cholera [ 43 , 71 , 72 , 74 , 75 ,…”