Zero Inflated Poisson and Geographically Weighted Zero- Inflated Poisson Regression Model: Application to Elephantiasis (Filariasis) Counts Data

Abstract

Poisson regression has been widely used for modeling counts data. Violation of equidispersion assumption can occur when there are excess of zeros of the data. For that condition we can use Zero-Inflated Poisson (ZIP) to analyze such data, resulting global parameter estimates. However spatial data from various locations have their own characteristics depend on their socio-cultural, geographical and economic conditions. In this paper, we first review the theoretical framework of Zero-Inflated Poisson (ZIP) and Geographically Weighted Zero Inflated Poisson (GWZIP) regression. We use Maximum Likelihood (MLE) method and EM algorithm to estimate the model parameters. The F test is used to compare the two models. Second, we fit these models to the number of filariasis case of East Java. In our case, there is the preponderance of zeros in the data set (65.79%). The results prove that the spatial dependence is absent, but there is weak spatial heterogeneity of the data (significance level α = 0.1). Based on F test, ZIP and GWZIP regression are not significantly different.

Towards global elimination of lymphatic filariasis: a systematic review of the application of spatial epidemiological methods to enhance surveillance and support elimination programmes

“…Two studies 17 18 examined but did not identify any spatial dependence in their dataset. One study 19 did not identify spatial autocorrelation, but identified weak spatial heterogeneity. Based on these results, the authors decided to incorporate spatial terms in the model to predict LF prevalence.…”

Analisis Jumlah Kasus Malaria Di Wilayah Sumatera Menggunakan Geographically Weighted Zero-Inflated Poisson Regression (Gwzipr)

“…Terdapatnya faktor spasial atau geografis dalam penyebaran malaria di berbagai wilayah. Perbedaan kondisi geografis di setiap wilayah, karakteristik masyarakat dan perekonomian masing-masing wilayah dapat menyebabkan adanya heterogenitas spasial (Purhadi et al, 2015). Untuk kasus yang terdapat heterogenitas spasial, maka metode tersebut dikembangkan dengan menambahkan faktor spasial yaitu Geographically Weighted Zero-Inflated Poisson Regression (GWZIPR).…”

Pendekatan Geographically Weighted Zero Inflated Poisson Regression (Gwzipr) Dengan Pembobot Fixed Bisquare Kernel Pada Kasus Difteri Di Indonesia

“…Penelitian terkait dengan GWZIPR telah dilakukan sebelumnya oleh Kalogirou [9] dengan hasil penelitian bahwa metode GWZIPR terbukti secara empiris lebih baik dalam menjelaskan tujuan para migran internal di Athena dibandingkan dengan ZIP. Penelitian lainnya dilakukan oleh Purhadi dkk [12] yang menunjukkan hasil berdasarkan uji F,GWZIPR dan ZIPR tidak berbeda secara signifikan. Adeliana [10] pada skripsinya mengestimasi parameter GWZIPR menggunakan data penderita penyakit tetanus neonatorum di seluruh kabupaten/kota di Provinsi Jawa Timur yang dipengaruhi oleh empat faktor secara signifikan yaitu cakupan imunisasi TT2+ terhadap jumlah ibu hamil, ibu bersalin ditolong tenaga kesehatan, cakupan kunjungan neonatal lengkap terhadap jumlah bayi, dan penangan komplikasi neonatal terhadap jumlah ibu hamil.…”