Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

PENERAPAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) BOX-JENKINS DALAM MERAMALKAN TINGKAT INFLASI DI PROVINSI ACEH

Inflasi merupakan kenaikan harga barang dan jasa secara umum yang terjadi secara terus menerus dalam jangka waktu tertentu. Pemerintah dalam suatu negara maupun daerah, perlu menelaah dan memperhatikan data inflasi pada masa lalu untuk mengetahui pergerakan nilai inflasi di suatu daerah. Tujuan penelitian ini adalah untuk meramalkan nilai inflasi di Provinsi Aceh pada periode September 2021 sampai Januari 2022 dengan menggunakan metode Autoregressive Integrated Moving Average (ARIMA) Box-Jenkins. Model terbaik yang diperoleh pada penelitian ini adalah model ARIMA(2,0,2) yang memiliki nilai keakuratan peramalan cukup baik. Keakuratan yang diukur dengan menggunakan nilai RMSE (Root of Mean Square) dan MAE (Mean Absolute Error) adalah mendekati nol, secara berturut-turut yaitu 0.474 dan 0.373. Hasil ramalan dari nilai inflasi pada periode ini tergolong ke dalam kategori inflasi ringan, dimana kenaikan harga barang yang terjadi pada periode tersebut masih dibawah angka 10% sehingga tidak berdampak pada perekonomian daerah.Kata Kunci : Peramalan, ARIMA Box-Jenkins, Inflas