Mobile phone handover data for measuring and analysing human population mobility in Western Ethiopia: implication for malaria disease epidemiology and elimination efforts

Abstract

Background Human mobility behaviour modelling plays an essential role in the understanding and control of the spread of contagious diseases by limiting the contact among individuals, predicting the spatio-temporal evolution of an epidemic and inferring migration patterns. It informs programmatic and policy decisions for effective and efficient intervention. The objective of this research is to assess the human mobility pattern and analyse its implication for malaria disease epidemiology. Methods In this study, human mobility patterns in Benishangul-Gumuz and Gambella regions in Western Ethiopia were explored based on a cellular network mobility parameter (e.g., handover rate) via real world data. Anonymized data were retrieved for mobile active users with mobility related information. The data came from anonymous traffic records collected from all the study areas. For each cell, the necessary mobility parameter data per hour, week and month were collected. A scale factor was computed to change the mobility parameter value to the human mobility pattern. Finally, the relative human mobility probability for each scenario was estimated. MapInfo and Matlab softwares were used for visualization and analysis purposes. Hourly travel patterns in the study settings were compared with hourly malaria mosquito vector feeding behaviour. Results Heterogeneous human movement patterns were observed in the two regions with some areas showing typically high human mobility. Furthermore, the number of people entering into the two study regions was high during the highest malaria transmission season. Two peaks of hourly human movement, 8:00 to 9:00 and 16:00 to 18:00, emerged in Benishangul-Gumuz region while 8:00 to 10:00 and 16:00 to 18:00 were the peak hourly human mobility time periods in Gambella region. The high human movement in the night especially before midnight in the two regions may increase the risk of getting mosquito bite particularly by early biters depending on malaria linked human behaviour of the population. Conclusions High human mobility was observed both within and outside the two regions. The population influx and efflux in these two regions is considerably high. This may specifically challenge the transition from malaria control to elimination. The daily mobility pattern is worth considering in the context of malaria transmission. In line with this malaria related behavioural patterns of humans need to be properly addressed.

International border malaria transmission in the Ethiopian district of Lare, Gambella region: implications for malaria spread into South Sudan

“…There are significant movement of people in and out of Gambella Region during months of high malaria transmission [ 38 ]. This can lead to the movement of parasites within and outside of the region.…”

“…Moreover, the local movement of folks during times of the day when mosquito vectors are active was also significant. This will lead to residual transmission, even if there is widespread coverage and use of bed nets in the area [ 38 ]. Besides, noticeable international border crossings along the Lare route into South Sudan (personal communication and observation) facilitate malaria spread through parasite transportation thereby challenging malaria control and elimination efforts in the Horn of Africa.…”

Visualization of Spatial–Temporal Epidemiological Data: A Scoping Review

“…The studies that used a map-style representation (79/114) or choropleth maps [17,23,24,27,30,32,34,35,37,40,46,48,49,52,55,56,58,60,62,64,68,71,[76][77][78][79]82,84,87,99,103,105,111,[113][114][115][116][119][120][121]124,127]. Choropleth maps are widely used to visualize the state of a variable across a geographic area.…”

“…Choropleth maps are widely used to visualize the state of a variable across a geographic area. In the papers, this technique was mostly used to represent the number of cases or cumulative cases in an area [17,22,24,27,46,48,52,56,58,[60][61][62][63]65,67,71,76,79,84,87,99,103,105,[113][114][115][116]119,121,127], along with the incidence of a disease [30,32,56,62,63,105,119,120,127]. It was also used to a lesser extent to visualize other measures, such as prevalence [35,64,76] and morbidity [111].…”

“…According to our review, more than half of the 114 studies (67/114) used 2D or 3D line charts as depictions of time series to represent temporal data related to a pan-demic. They were not only used to depict the number of cases per week, month, or year [16,21,22,24,25,30,36,50,53,54,56,59,60,62,65,67,68,[71][72][73]75,79,[84][85][86][87]97,98,100,103,104,106,109,113,114,120,121,127] or cumulative cases [22,27,52,55,65,70,71,[98][99][100]105,…”