Epidemiological analysis of spatially misaligned data: a case of highly pathogenic avian influenza virus outbreak in Nigeria

Abstract

SummaryThis research is focused on the epidemiological analysis of the transmission of the highly pathogenic avian influenza (HPAI) H5N1 virus outbreak in Nigeria. The data included 145 outbreaks together with the locations of the infected farms and the date of confirmation of infection. In order to investigate the environmental conditions that favoured the transmission and spread of the virus, weather stations were realigned with the locations of the infected farms. The spatial KolmogorovSmirnov test for complete spatial randomness rejects the null hypothesis of constant intensity (P < 0·0001). Preliminary exploratory analysis showed an increase in the incidence of H5N1 virus at farms located at high altitude. Results from the Poisson log-linear conditional intensity function identified temperature (−0·9601) and wind speed (0·6239) as the ecological factors that influence the intensity of transmission of the H5N1 virus. The model also includes distance from the first outbreak (−0·9175) with an Akaike's Information Criterion of −103·87. Our analysis using a point process model showed that geographical heterogeneity, seasonal effects, temperature, wind as well as proximity to the first outbreak are very important components of spread and transmission of HPAI H5N1.

Characteristics of commercial and traditional village poultry farming in Mali with a focus on practices influencing the risk of transmission of avian influenza and Newcastle disease

“…Stevens et al 2013, Adegboye andKotze 2014). Our results confirm that the majority (61.0%) of farms are located in the vicinity (≤50 km) of the Bamako, capital city of Mali(FAO, 2006).…”

Spatially Correlated Time Series and Ecological Niche Analysis of Cutaneous Leishmaniasis in Afghanistan

“…The geographical variations in the species occurrences are often profoundly favoured by certain climatic and environmental constraints [14,15]. In ENM, the observed presence data (and pseudo-absence data) together with ecological variables at the sample region are used to provide a reasonable likelihood of the species being present at all other locations [15,25].…”

Spatially Correlated Time Series and Ecological Niche Analysis of Cutaneous Leishmaniasis in Afghanistan

“…Ecological niche modelling (ENM) provides a way to relate the geographical distributions of species to ecologic niches [14]. ENM is a growing area in spatial analysis of disease epidemiology [14,15], characterizing the distribution of the disease in a space defined by environmental parameters [15]. The use of ENM under the framework of geographic information system in spatial disease modelling cannot be overemphasized.…”

“…For example, Du et al [16] used ENM to identify the potential high risk areas for severe fever with thrombocytopenia syndrome (SFTS) in China. Adegboye and Kotze [15] used ENM to explore the geographical constraints that may favour the outbreak of the H5N1 virus in Nigeria. Similarly, ENM was used to estimate the current niche and potential distribution of mycetoma in Sudan and South Sudan [17], and to predict the zoonotic transmission niche of Ebola covering countries across Central and West Africa [18].…”