Aims We aimed at giving a preliminary analysis of the weakness of a current test strategy, and proposing a data-driven strategy that was self-adaptive to the dynamic change of pandemic. The effect of driven-data selection over time and space was also within the deep concern. Methods A mathematical definition of the test strategy were given. With the real COVID-19 test data from March to July collected in Lahore, a significance analysis of the possible features was conducted. A machine learning method based on logistic regression and priority ranking were proposed for the data-driven test strategy. With performance assessed by the area under the receiver operating characteristic curve(AUC), time series analysis and spatial cross-test were conducted. Results The transition of risk factors accounted for the failure of the current test strategy. The proposed data-driven strategy could enhance the positive detection rate from 2.54% to 28.18%, and the recall rate from 8.05% to 89.35% under strictly limited test capacity. Much more optimal utilization of test resources could be realized where 89.35% of total positive cases could be detected with merely 48.17% of the original test amount. The strategy showed self-adaptability with the development of pandemic, while the strategy driven by local data was proved to be optimal. Conclusions We recommended a generalization of such a data-driven test strategy for a better response to the global developing pandemic. Besides, the construction of the COVID-19 data system should be more refined on space for local applications.

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Virus spreading and its mitigation is an important safety issue that has drawn wide attention of many countries and people. For researchers in this area, it is an interesting work to study virus spreading with safety theories and methods. In this paper, we worked on the spatial extent of SIR model, which considers the known facts of Covid-19 behavior i.e. its spreading extent with time, the total population of area concerned and dedicated health facilities. Also, a special relationship between Covid-19 cases and NLDI data driven by night-time satellite imagery is being discussed. Results predicted a huge gap between predicted and presently available facilities for number of hospitals, beds, and ventilators. Findings suggest that developing countries like our study area Lahore District, Pakistan needs to follow social distancing at immense level, which not only helps in reducing the numbers of infections and fatalities but also the time duration of the whole epidemic. Maps based on NLDI vales, predicted cases, hospitals and ventilators needs could be greatly helpful for policymakers to analyze situation and concentrate on areas which needs immediate attention. Dealing with the pandemic requires a pre-planned command and control structure that could make quick and informed decisions in the whole city. We recommend that the use of proper model prediction at Union Council level can help local government in policymaking related social distancing and healthcare systems. The decision of social distancing should be on time and like what percent of social distancing is needed, which tackle with the already available health care structure.

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As the climate changes with the population expansion in Pakistan, wildfires are becoming more threatening. The goal of this study was to understand fire trends which might help to improve wildland management and reduction in wildfire risk in Pakistan. Using descriptive analyses, we investigated the spatiotemporal trends and causes of wildfire in the 2001–2020 period. Optimized machine learning (ML) models were incorporated using variables representing potential fire drivers, such as weather, topography, and fuel, which includes vegetation, soil, and socioeconomic data. The majority of fires occurred in the last 5 years, with winter being the most prevalent season in coastal regions. ML models such as RF outperformed others and correctly predicted fire occurrence (AUC values of 0.84–0.93). Elevation, population, specific humidity, vapor pressure, and NDVI were all key factors; however, their contributions varied depending on locational clusters and seasons. The percentage shares of climatic conditions, fuel, and topographical variables at the country level were 55.2%, 31.8%, and 12.8%, respectively. This study identified the probable driving factors of Pakistan wildfires, as well as the probability of fire occurrences across the country. The analytical approach, as well as the findings and conclusions reached, can be very useful to policymakers, environmentalists, and climate change researchers, among others, and may help Pakistan improve its wildfire management and mitigation.