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Sustainability ; 14(15):9715, 2022.
Article in English | ProQuest Central | ID: covidwho-1994199


Land-use transition is one of the most profound human-induced alterations of the Earth’s system. It can support better land management and decision-making for increasing the yield of food production to fulfill the food needs in a specific area. However, modeling land-use change involves the complexity of human drivers and natural or environmental constraints. This study develops an agent-based model (ABM) for land use transitions using critical indicators that contribute to food deserts. The model’s performance was evaluated using Guilford County, North Carolina, as a case study. The modeling inputs include land covers, climate variability (rainfall and temperature), soil quality, land-use-related policies, and population growth. Studying the interrelationships between these factors can improve the development of effective land-use policies and help responsible agencies and policymakers plan accordingly to improve food security. The agent-based model illustrates how and when individuals or communities could make specific land-cover transitions to fulfill the community’s food needs. The results indicate that the agent-based model could effectively monitor land use and environmental changes to visualize potential risks over time and help the affected communities plan accordingly.

One Health ; 14: 100371, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1900050


Since the beginning of the COVID-19 pandemic in early 2020, global efforts to respond to and control COVID-19 have varied widely with some countries, including Australia, successfully containing local transmission, and minimising negative impacts to health and economies. Over this time, global awareness of climate variability due to climate change and the risk factors for emerging infectious diseases transmission has increased alongside an understanding of the inextricable relationship between the health of the environment, humans, and animals. Overall, the global response to the current pandemic suggests there is an urgent need for a One Health approach in controlling and preventing future pandemics, through developing integrated, dynamic, spatiotemporal early warning systems based on a One Health approach for emerging infectious diseases.

Geohealth ; 5(10): e2020GH000378, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1537285


Many of the respiratory pathogens show seasonal patterns and association with environmental factors. In this article, we conducted a cross-sectional analysis of the influence of environmental factors, including climate variability, along with development indicators on the differential global spread and fatality of COVID-19 during its early phase. Global climate data we used are monthly averaged gridded data sets of temperature, humidity and temperature anomaly. We used Human Development Index (HDI) to account for all nation wise socioeconomic factors that can affect the reporting of cases and deaths and build a stepwise negative binomial regression model. In the absence of a development indicator, all environmental variables excluding the specific humidity have a significant association with the spread and mortality of COVID-19. Temperature has a weak negative association with COVID-19 mortality. However, HDI is shown to confound the effect of temperature on the reporting of the disease. Temperature anomaly, which is being regarded as a global warming indicator, is positively associated with the pandemic's spread and mortality. Viewing newer infectious diseases like SARS-CoV-2 from the perspective of climate variability has a lot of public health implications, and it necessitates further research.

SN Comput Sci ; 2(6): 452, 2021.
Article in English | MEDLINE | ID: covidwho-1410913


COVID-19, a life-threatening infection by novel coronavirus, has broken out as a pandemic since December 2019. Eventually, with the aim of helping the World Health Organization and other health regulators to combat COVID-19, significant research effort has been exerted during last several months to analyze how the various factors, especially the climatic aspects, impact on the spread of this infection. However, due to insufficient test and lack of data transparency, these research findings, at times, are found to be inconsistent as well as conflicting. In our work, we aim to employ a semantics-driven probabilistic framework for analyzing the causal influence as well as the impact of climate variability on the COVID-19 outbreak. The idea here is to tackle the data inadequacy and uncertainty issues using probabilistic graphical analysis along with embedded technology of incorporating semantics from climatological domain. Furthermore, the theoretical guidance from epidemiological model additionally helps the framework to better capture the pandemic characteristics. More significantly, we further enhance the impact analysis framework with an auxiliary module of measuring semantic relatedness on regional basis, so as to realistically account for the existence of multiple climate types within a single spatial region. This added notion of regional semantic relatedness further helps us to attain improved probabilistic analysis for modeling the climatological impact on this disease outbreak. Experimentation with COVID-19 datasets over 15 states (or provinces) belonging to varying climate regions in India, demonstrates the effectiveness of our semantically-enhanced theory-guided data-driven approach. It is worth noting that our proposed framework and the relevant semantic analyses are generic enough for intelligent as well as explainable impact analysis in many other application domains, by introducing minimal augmentation.