Risk Mapping of Influenza D Virus Occurrence in Ruminants and Swine in Togo Using a Spatial Multicriteria Decision Analysis Approach

Abstract

Influenza D virus (IDV) has been identified in several continents, with serological evidence for the virus in Africa. In order to improve the sensitivity and cost–benefit of IDV surveillance in Togo, risk maps were drawn using a spatial multicriteria decision analysis (MCDA) and experts’ opinion to evaluate the relevance of sampling areas used so far. Areas at highest risk of IDV occurrence were the main cattle markets. The maps were evaluated with previous field surveillance data collected in Togo between 2017 and 2019: 1216 sera from cattle, small ruminants, and swine were screened for antibodies to IDV by hemagglutination inhibition (HI) assays. While further samples collections are needed to validate the maps, the risk maps resulting from the spatial MCDA approach generated here highlight several priority areas for IDV circulation assessment.

The first decade of research advances in influenza D virus

“…This work was further confirmed by another serological survey in Ethiopia, where a high seroprevalence of IDV was also observed in dromedary camels [49]. In sum, IDV infection has been demonstrated in several animal species in Africa through serological evidence, but the virus has not yet been isolated on this continent [21, 49, 50]. Future studies, such as virus isolation and antigenic characterization, are probably needed to fully illustrate the role of African animals in replication and transmission of IDV.…”

“…Continuedbut the virus has not yet been isolated on this continent[21,49,50]. Future studies, such as virus isolation and antigenic characterization, are probably needed to fully illustrate the role of African animals in replication and transmission of IDV.…”

An Interoperable Architecture for the Internet of COVID-19 Things (IoCT) Using Open Geospatial Standards—Case Study: Workplace Reopening

“…To effectively integrate the multi-sensor system for cleaning risk analysis, a multi-criteria evaluation [ 50 ] was applied to identify, and rank COVID-19 risky behaviors based on an available multi-sensor system in the CCIT building at the University of Calgary campus. Since 80 percent of the data used by the proposed IoCT system was geospatially related, Spatial Multi-Criteria Decision Analysis (SMCDA) provided a superior framework for a variety of decision-making situations [ 51 , 52 , 53 ].…”

Regional infectious risk prediction of COVID-19 based on geo-spatial data

“…In the real epidemic prevention and control, population migration and motion can spread the epidemic rapidly, so it is an essential factor in the epidemic situation (Wong et al, 2020;Yue & Clayton, 2011). In this article, we introduced these factors into the spatial risk map theory to measure the distance between the regions (Pourghasemi et al, 2020;Ijumulana et al, 2020;Fusade-Boyer et al, 2020) and dynamically measure the spread of the epidemic in China. Pourghasemi et al (2020) used a spatial model and risk map to analyze the epidemic situation in Iran from January 19, 2020, to July 14, 2020.…”