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[This corrects the article DOI: 10.1016/j.mex.2024.102841.].
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Land-use modeling stands as a pivotal tool in shaping sustainable development policies. With the rapid advancement of remote-sensing technology and the widespread adoption of satellite imagery-based land cover products, these datasets have emerged as primary sources for understanding land-use dynamics due to their high spatial and temporal resolutions. Yet, it remains challenging to effectively integrate such rich panel data into nonlinear econometric land-use models. This paper introduces a method to seamlessly incorporate land cover panel data into econometric models, enabling comprehensive utilization of temporal information within a single framework.-By capturing dynamic land-use patterns, the method enhances prediction accuracy while mitigating issues such as autocorrelated error terms commonly encountered in panel data analysis.-The method is straightforward to implement and applicable to many nonlinear models, making it particularly suitable for datasets with large sample sizes.
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BackgroundSince its first report on March 08, COVID-19 positive cases and number of deaths are increasing in Bangladesh. In the first month of COVID-19 infection, incidence of daily positive cases did follow the susceptible, infected and recovered (SIR) based predictions we reported in April, but started to deviate in the following months. COVID-19 transmission and disease progression depends on multifaceted determinants e.g. viral genetics, host immunity, social distancing, co-morbidity, socio-demographic and environmental parameters. Therefore deviation in confirmed cases from predicted model may appear and warrant thorough investigation. MethodsIn this short report, we compared real data with SIR model and analyzed the possible factors associated with the deviation which included preventive intervention strategies, socioeconomic capabilities, climatic and meteorological indexes, acquired immunity of Bangladeshi population, demographic characteristics, health indicators and food habits. ResultsThe key factor responsible for the observed deviation was found to be the number of tests performed. Having population with low median age, young age groups are being mostly infected. Low prevalence of non-communicable diseases among them and strong immunity compared to the elderly might have kept most of them asymptomatic with silent recovery. Warm temperature, humidity and UV index of Bangladesh during this summer period might have contributed to the slow progression of infection. Longer daylight mediated immunity, fresh air circulations and ventilation, less population density in rural areas and certain food habits perhaps helped the large number of populations to restrict the infection up to a level. ConclusionDespite all these helpful determinants in Bangladesh, person to person contact is still the leading risk factor for COVID-19 transmission. Infection may increase rapidly if safe distance and preventive measures are not strictly followed while resuming the normal social and work life. Expanding test capacity, strong collaborative action plans, strategies and implementation are needed immediately to prevent catastrophe. HighlightsO_LILimited number of tests compared to large population was the key reason for possible low daily positive cases reported in Bangladesh. C_LIO_LIControlled interventions viz. official leave; transport ban and social distancing had helped initially to slow down the transmission. C_LIO_LIWarm weather, high humidity and UV index, sunlight mediated immunity, fresh air circulations, low pollutions, food habit and heterologous immunity might have reduced the transmission capabilities of SARS-CoV-2. C_LIO_LIHaving large number of young people with strong immunity might have kept most of the infected asymptomatic who recovered silently. C_LIO_LIPerson to person contact still remain as key risk factor in COVID-19 transmission, so strict health measures should be in place even after reopening social activities to contain further transmission. C_LI
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Human race has often faced pandemic with substantial number of fatalities. As COVID-19 pandemic reached and endured in every corner on earth, countries with moderate to strong healthcare support and expenditure seemed to struggle in containing disease transmission and casualties. COVID-19 affected countries have variability in demographic, socioeconomic and life style health indicators. At this context it is important to find out at what extent these parametric variations are actually modulating disease outcomes. To answer this, we have selected demographic, socioeconomic and health indicators e.g. population density, percentage of urban population, median age, health expenditure per capita, obesity, diabetes prevalence, alcohol intake, tobacco use, case fatality of non communicable diseases (NCDs) as independent variables. Countries were grouped according to these variables and influence on dependent variables e.g. COVID-19 test positive, case fatality and case recovery rates were statistically analyzed. The results suggest that countries with variable median age has significantly different outcome on test positive rate (P<0.01). Both median age (P=0.0397) and health expenditure per capita (P=0.0041) has positive relation with case recovery. Increasing number of test per 100K population showed positive and negative relation with number of positives per 100K population (P=0.0001) and percentage of test positives (P<0.0001) respectively. Alcohol intake per capita in liter (P=0.0046), diabetes prevalence (P=0.0389) and NCDs mortalities (P=0.0477) also showed statistical relation with case fatality rate. Further analysis revealed that countries with high healthcare expenditure along with high median age and increased urban population showed more case fatality but also had better recovery rate. Investment in health sector alone is insufficient in controlling pandemic severity. Intelligent and sustainable healthcare both in urban and rural settings and healthy lifestyle acquired immunity may reduce disease transmission and comorbidity induced fatalities respectively.
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The COVID-19 pandemic has a devastating impact on the health and well-being of global population. Cough audio signals classification showed potential as a screening approach for diagnosing people, infected with COVID-19. Recent approaches need costly deep learning algorithms or sophisticated methods to extract informative features from cough audio signals. In this paper, we propose a low-cost envelope approach, called CovidEnvelope, which can classify COVID-19 positive and negative cases from raw data by avoiding above disadvantages. This automated approach can pre-process cough audio signals by filter-out back-ground noises, generate an envelope around the audio signal, and finally provide outcomes by computing area enclosed by the envelope. It has been seen that reliable datasets are also important for achieving high performance. Our approach proves that human verbal confirmation is not a reliable source of information. Finally, the approach reaches highest sensitivity, specificity, accuracy, and AUC of 0.92, 0.87, 0.89, and 0.89 respectively. The automatic approach only takes 1.8 to 3.9 minutes to compute these performances. Overall, this approach is fast and sensitive to diagnose the people living with COVID-19, regardless of having COVID-19 related symptoms or not, and thus have vast applicability in human well-being by designing HCI devices incorporating this approach.
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BackgroundCOVID-19 is transmitting worldwide drastically and infected nearly two and half million of people so far. Till date 2144 cases of COVID-19 is confirmed in Bangladesh till 18th April though the stage-3/4 transmission is not validated yet. MethodsTo project the final infection numbers in Bangladesh we used the SIR mathematical model. Confirmed cases of infection data were obtained from Institute of Epidemiology, Disease Control and Research (IEDCR) of Bangladesh ResultsThe confirmed cases in Bangladesh follow our SIR model prediction cases. By the end of April the predicted cases of infection will be 17450 to 21616 depending on the control strategies. Due to large population and socio-economic characteristics, we assumed 60% social distancing and lockdown can be possible. Assuming that, the predicated final size of infections will be 3782558 on the 92th day from the first infections and steadily decrease to zero infection after 193 days ConclusionTo estimate the impact of social distancing we assumed eight different scenarios, the predicted results confirmed the positive impact of this type of control strategies suggesting that by strict social distancing and lockdown, COVID-19 infection can be under control and then the infection cases will steadily decrease down to zero.