“Seeing you every day!”: Understanding disease dynamics using Panel Regression Approach
“Seeing you every day!”: Understanding disease dynamics using Panel Regression Approach
Embedded Files
Dr. Pedro A. Alviola IV and Mr. Zython Paul T. Lachica
Dr. Pedro A. Alviola IV and Mr. Zython Paul T. Lachica
Disease information such as incidence, detection, mortality, etc., at a smaller cross-section unit (e.g. individuals, households) can be linked to a set of environmental drivers (i.e., climate variables) as well as socio-demographic factors (e.g. household size, income, health practices). More data collected can give us a clearer picture of how these drivers explain the changes in the response variables. Suppose that we have an epidemiologic dataset that contains detection information of a disease per cross-section over a specified time period. We found that on period t there is a positive detection of the disease. In period t + 1 a negative detection was recorded but the disease was detected again in period t + 2. Now we are interested in analyzing the effects of these identified drivers on the likelihood of an occurrence of a disease given information on disease detections over a specified time-periods. This workshop introduces the use of the Panel Regression Framework as a statistical model to analyze and predict the effects of drivers on a response variable. In this workshop, we will attempt to use this model framework to analyze the effects of disease drivers on the likelihood of Rotavirus-A (RVA) occurrence in swine farms.
Disease information such as incidence, detection, mortality, etc., at a smaller cross-section unit (e.g. individuals, households) can be linked to a set of environmental drivers (i.e., climate variables) as well as socio-demographic factors (e.g. household size, income, health practices). More data collected can give us a clearer picture of how these drivers explain the changes in the response variables. Suppose that we have an epidemiologic dataset that contains detection information of a disease per cross-section over a specified time period. We found that on period t there is a positive detection of the disease. In period t + 1 a negative detection was recorded but the disease was detected again in period t + 2. Now we are interested in analyzing the effects of these identified drivers on the likelihood of an occurrence of a disease given information on disease detections over a specified time-periods. This workshop introduces the use of the Panel Regression Framework as a statistical model to analyze and predict the effects of drivers on a response variable. In this workshop, we will attempt to use this model framework to analyze the effects of disease drivers on the likelihood of Rotavirus-A (RVA) occurrence in swine farms.
Prior reading/prerequisites
Prior reading/prerequisites
Prerequisites:
1. R Programming and Gretl handling; and
Prerequisites:
1. R Programming and Gretl handling; and
2. Basic knowledge in regression analysis (e.g. simple linear regression, multiple linear regression)
2. Basic knowledge in regression analysis (e.g. simple linear regression, multiple linear regression)
Reading Materials:
1. Murao, L.A.E., Bacus, M.G., Junsay, N.X.T., Albarillo, D.L.D., Otero, M.C.B., Buenaventura, S.G.C., Ligue, K.D.B. and Alviola, P.A., 2019. Spatiotemporal dynamics and risk factors of rotavirus A circulation in backyard pig farms in a Philippine setting. Tropical animal health and production, 51(4), pp.929-937. Source:
Reading Materials:
1. Murao, L.A.E., Bacus, M.G., Junsay, N.X.T., Albarillo, D.L.D., Otero, M.C.B., Buenaventura, S.G.C., Ligue, K.D.B. and Alviola, P.A., 2019. Spatiotemporal dynamics and risk factors of rotavirus A circulation in backyard pig farms in a Philippine setting. Tropical animal health and production, 51(4), pp.929-937. Source:
2. Thomsen, M.R., Nayga Jr, R.M., Alviola IV, P.A. and Rouse, H.L., 2016. The effect of food deserts on the body mass index of elementary schoolchildren. American Journal of Agricultural Economics, 98(1), pp.1-18. Source:
2. Thomsen, M.R., Nayga Jr, R.M., Alviola IV, P.A. and Rouse, H.L., 2016. The effect of food deserts on the body mass index of elementary schoolchildren. American Journal of Agricultural Economics, 98(1), pp.1-18. Source:
Page updated
Report abuse