Dengue is a mosquito-transmitted viral disease that causes mild to severe infection. In the tropics, the risk of high infection is driven by factors that influence its vector’s population density such as meteorological variables and unplanned rapid urbanization. In the Philippines, dengue remains endemic in all regions reporting hundreds of thousands of cases annually. The continuous development of data-based infectious disease prediction models plays a vital role in overcoming this persistent adversity. This study explored the application of artificial intelligence (AI) through a deep learning approach using the long short-term memory (LSTM) architecture in our prediction model. This is compared with the traditional feed-forward network approach using a multilayer perceptron (MLP) model and a statistical approach using non-seasonal and seasonal autoregressive integrated moving average (ARIMA). The forecasting models predicted the monthly number of reported dengue cases in Davao City using temperature, rainfall, relative humidity, and previous monthly cases. Model performance was evaluated using the root mean square error (RMSE). The LSTM model recorded the highest accuracy among the models, reporting two times lower RMSE than the MLP model and four times lower RMSE than the statistical models. This result demonstrated the feasibility of deep learning techniques to capture nonlinear characteristics of data and the ability of the LSTM to effectively incorporate information from longer past periods in its prediction.

Emerging infectious diseases (EIDs) – influenced by socioeconomic, environmental, andecological factors – pose a significant threat to both global economies and public health.Multiple EIDs have emerged in the 21st century, including the current coronavirus disease(COVID-19) pandemic associated with a novel severe acute respiratory syndrome coronavirus2 (SARS-CoV2). Understanding the factors contributing to the emergence of EIDs is crucialfor mitigating its risks and preventing disease outbreaks that could escalate into epidemics andpandemics. Thus, this study developed a scoring index that could estimate the vulnerability ofan area concerning the emergence of novel EIDs using established global predictors – whichinclude socio-economic and demographic factors (population density, housing, and businesspermits), weather variables (humidity, precipitation, and temperature), animal hosts (livestockand domestic animal populations), and wildlife-livestock-human interfaces (wet markets,urban green spaces, and commercial animal farms). Davao City, located in the Southern partof the Philippines, was used as a study site, wherein its barangays represent the smallest unitof geopolitical communities. Barangays vulnerable to EIDs were determined based on theresulting EID index. Clustering of vulnerability index scores across communities was identifiedusing the Global Moran’s I and Getis-Ord General G statistic. The Getis-Ord Gi* and AnselinLocal Moran’s I were then used to identify the hotspot barangays most vulnerable to EIDs. Themost vulnerable areas to EIDs were barangays with larger urban green spaces and a highernumber of commercial animal farms and wet markets in Davao City. These most vulnerablebarangays were also clustered and located in the districts of Toril, Tugbok, and Calinan. Hence,an appropriate basic action plan for these most vulnerable barangays should be provided.Moreover, policies related to wildlife management and urban planning should be revisited,with emphasis on mitigating the risks (i.e. infectious disease surveillance, zoning, animal biosecurity, and sanitation and hygiene) associated with EIDs while giving equal importancetoward maintaining a stable local economy.

Changes that occurred in the agriculture-food system – for instance, increase in disease incidence compounded by climate change, as well as changes in contractual arrangements and policies – pose challenges to small-scale Cavendish banana farmers in the Davao region, the top producer of Cavendish in the Philippines, in terms of their vulnerability and ability to survive. The current challenges can be addressed by increasing the opportunity of small-scale farmers to increase their profit by exploring alternative enterprises. The current system of the farmers is to allocate their entire harvested Cavendish bananas into the contractual market or the spot market. While these farmers can improve their profit by processing raw bananas into alternative products such as banana flour, very few of them have embarked on this enterprise. One reason for this is the limited understanding of this market potential and its profitability relative to contractual or spot market. Hence, in this study, we explored through a model simulation – a novel yet simplistic approach – different scenarios of a farmer’s profit as they venture into the banana flour market. Some model considerations include flour demand, the volatility of spot market prices, and banana production rates. Our simulations show that the total profit of the model farmer varies significantly with different allocations of bananas to contractual or spot markets for various demands in the banana flour market. Additionally, with our model assumptions, model farmers can further increase their profit if all their unsold fresh bananas have been processed to banana flour and sold them during high demand. Finally, we highlight the novelty of our approach as a diagnostic tool to initially assess the profitability of a commodity among different market options especially when there are unexplored scenarios and data is scarce.

According to the World Health Organization (WHO), the elderly and people with comorbidities are most vulnerable to COVID-19 infection. With this, the challenges and threats posed to the vulnerable population require targeted interventions. While public health surveillance methods had developed recent advances to meet users’ information needs, the volume and complexity of infectious disease data had increased, resulting in increasing difficulty to facilitate risk communication with the public and for decision-makers to make informed measures to protect the public’s health. Moreover, the implementation of COVID-19 spatiotemporal disease surveillance strategies specifically targeting the vulnerable population in the Davao Region had been previously unexplored. This paper investigated the COVID-19 incidence in the Davao Region from 03 Mar 2020, the earliest recorded date of onset, to 31 Aug 2021 using geospatial tools. The variables were visualized through choropleth maps and graduated symbols, and subsequently examined through spatial autocorrelation and hotspot analysis. Hotspots across the region were observed to be in high-density areas. These areas pose greater risks of infection due to the presence of a high concentration of cases. However, high case fatality rates were found in far-flung municipalities where access to COVID-19 healthcare facilities is a dilemma. In the COVID-19 setting and future disease outbreaks similar to COVID-19, results from this study may provide insights to government offices and other related agencies to improve healthcare systems and programs such as providing and initiating tailor-fitted isolation and consultation mechanisms appropriate to the vulnerable population in a community.