Linear Models for Multivariate, Time Series, and Spatial Data

Weak Convergence and Empirical Processes (A.W. van der Vaart, J.A. Wellner)Quasi-Likelihood and its Application. A General Approach to Parameter Estimation(C.C. Heyde)Wahrscheinlichkeitsrechnung und Statistik in Beispielen und Aufgaben (V. Nollau,L. Partzsch, R. Storm, C. Lange)A First Course in Multivariate Statistics (B. Flury)Empirische Forschungsmethoden (W. Stier)Applying Generalized Linear Models (J.K. Lindsey)Analyse von Tabellen und kategorialen Daten (H.J. Andreß, J.A.Hagenaars, S. Kühnel)Elements of Multivariate Time Series Analysis (G.C. Reinsel)Nonparametric Smoothing and Lack-of-Fit Tests (J.D. Hart)Modelling Extremal Events for Insurance and Finance (P. Embrechts, C. Klüppelberg,T. Mikosch)Statistical Analysis of Extreme Values (R.D. Reiss, M. Thomas)Das Quotenverfahren (A. Quatember)Prophetentheorie (F. Harten, A. Meyerthole, N. Schmitz)Advances in Combinational Methods and Applications to Probability and Statistics.(N. Balakrishnan)

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

10.20944/preprints201609.0098.v1 ◽

2016 ◽

Cited By ~ 1

Author(s):

Oyelola A. Adegboye ◽

Majeed Adegboye

Keyword(s):

Time Series ◽

Cutaneous Leishmaniasis ◽

Spatial Data ◽

Ecological Niche ◽

Ecological Niche Modeling ◽

Multivariate Time Series ◽

Steady Increase ◽

Niche Modelling ◽

Spatially Correlated ◽

Presence Data

Leishmaniasis is the third most common vector-borne disease and a very important protozoan infection. Cutaneous leishmaniasis is one of the most common types of leishmaniasis infectious diseases with up to 2 million occurrences of new cases each year worldwide. A dynamic transmission multivariate time series model was applied to the data to account for overdispersion and evaluate the effects of three environmental layers as well as seasonality in the data. Furthermore, ecological niche modeling was used to investigate the geographical suitable conditions for cutaneous leishmaniasis using temperature, precipitation and altitude as environmental layers, together with the leishmaniasis presence data. A retrospective analysis of the cutaneous leishmaniasis spatial data in Afghanistan between 2003 and 2009 indicates a steady increase from 2003 to 2007, a small decrease in 2008, then another increase in 2009. An upward trend and regularly repeating patterns of highs and lows was observed related to the months of the year which suggests seasonality effect in the data. Two peaks were observed in the disease occurrence-- January to March and September to December -- which coincide with the cold period. Ecological niche modelling indicates that precipitation has the greatest contribution to the potential distribution of leishmaniasis.

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