Agricultural production is a major driver of the Philippine economy. Mass production of animal products, such as livestock and poultry farming, is one of the most prominent players in the field. Filipino farmers use veterinary medicinal products (VMPs) when raising agricultural animals to improve animal growth and prevent diseases. Unfortunately, the extensive use of VMPs, particularly antibiotics, has been linked to drug resistance in animals, particularly antibiotics. Antimicrobial gene products produced in animals due to the prolonged use of VMPs can passed on to humans when they consume animal products.This paper reviews information on the use of VMPs in the Philippines, including the regulations, their impact, challenges, and potential recommendations. The Philippines has existing legislation regulating VMP use. Several agencies were tasked to regulate the use of VMPs, such as the Department of Agriculture, the Department of Health, and the Philippine National Action Plan. Unfortunately, there is a challenge to implementing these regulations, which affects consumers. The unregulated use of VMPs influences the transmission of antibiotic residues from animals to crops to humans. This challenge should be addressed, with more focus on stricter regulation.

Objective: To correlate climatic and environmental factors such as land surface temperature, rainfall, humidity and normalized difference vegetation index with the incidence of dengue to develop prediction models for the Philippines using remote-sensing data. Methods: Time-series analysis was performed using dengue cases in four regions of the Philippines and monthly climatic variables extracted from Global Satellite Mapping of Precipitation for rainfall, and MODIS for the land surface temperature and normalized difference vegetation index from 2008-2015. Consistent dataset during the period of study was utilized in Autoregressive Integrated Moving Average models to predict dengue incidence in the four regions being studied. Results: The best-fitting models were selected to characterize the relationship between dengue incidence and climate variables. The predicted cases of dengue for January to December 2015 period fitted well with the actual dengue cases of the same timeframe. It also showed significantly good linear regression with a square of correlation of 0.869 5 for the four regions combined. Conclusion: Climatic and environmental variables are positively associated with dengue incidence and suit best as predictor factors using Autoregressive Integrated Moving Average models. This finding could be a meaningful tool in developing an early warning model based on weather forecasts to deliver effective public health prevention and mitigation programs.