Earth observation(EO) technologies,such as very high-resolution optical satellite data available from Maxar,can enhance economic consequence modeling of disasters by capturing the fine-grained and real-time behavioral...Earth observation(EO) technologies,such as very high-resolution optical satellite data available from Maxar,can enhance economic consequence modeling of disasters by capturing the fine-grained and real-time behavioral responses of businesses and the public.We investigated this unique approach to economic consequence modeling to determine whether crowd-sourced interpretations of EO data can be used to illuminate key economic behavioral responses that could be used for computable general equilibrium modeling of supply chain repercussions and resilience effects.We applied our methodology to the COVID-19 pandemic experience in Los Angeles County,California as a case study.We also proposed a dynamic adjustment approach to account for the changing character of EO through longer-term disasters in the economic modeling context.We found that despite limitations,EO data can increase sectoral and temporal resolution,which leads to significant differences from other data sources in terms of direct and total impact results.The findings from this analytical approach have important implications for economic consequence modeling of disasters,as well as providing useful information to policymakers and emergency managers,whose goal is to reduce disaster costs and to improve economic resilience.展开更多
Objective: To describe the demographic and evolutionary characteristics of pregnant and postpartum women with coronavirus disease 2019 (COVID-19) admitted to a medium-sized hospital in Brazil. Method: This is a descri...Objective: To describe the demographic and evolutionary characteristics of pregnant and postpartum women with coronavirus disease 2019 (COVID-19) admitted to a medium-sized hospital in Brazil. Method: This is a descriptive and retrospective study, collected from medical records, from March 2020 to October 2021 in a hospital located in Cuiabá (MT). Results: Pregnant and puerperal women with COVID-19 who needed hospitalization were mixed-race, from the metropolitan area, and carriers of moderate and severe forms of the disease. The primary risk condition found was overweight/obesity, and pre-gestational diabetes, hypertension, asthma, and autoimmune disease were the most prevalent comorbidities in the group. Elevated lactate dehydrogenase (LDH), c-reactive protein (CRP), and D-dimer were relevant laboratory findings in this group of patients. The most frequent maternal outcomes were respiratory failure, invasive ventilatory support, thromboembolic phenomena, sepsis, and preterm labor. Maternal death occurred in 6.4% of pregnant women. Most maternal deaths were of women who lived in the interior of the state, and the minority arrived on adequate ventilatory support. Prematurity and the need for neonatal intensive care unit (NICU) were significant complications for neonates. Stillbirth/neonatal mortality occurred in 11.0%. Conclusion: The clinical conditions at hospitalization were associated with worse living conditions and lack of access to health care, resulting in increased chances of severity and worsening outcomes in this group of women and neonates.展开更多
基金funded by the NASA Disasters Program grant#NH18ZDA001N001N.
文摘Earth observation(EO) technologies,such as very high-resolution optical satellite data available from Maxar,can enhance economic consequence modeling of disasters by capturing the fine-grained and real-time behavioral responses of businesses and the public.We investigated this unique approach to economic consequence modeling to determine whether crowd-sourced interpretations of EO data can be used to illuminate key economic behavioral responses that could be used for computable general equilibrium modeling of supply chain repercussions and resilience effects.We applied our methodology to the COVID-19 pandemic experience in Los Angeles County,California as a case study.We also proposed a dynamic adjustment approach to account for the changing character of EO through longer-term disasters in the economic modeling context.We found that despite limitations,EO data can increase sectoral and temporal resolution,which leads to significant differences from other data sources in terms of direct and total impact results.The findings from this analytical approach have important implications for economic consequence modeling of disasters,as well as providing useful information to policymakers and emergency managers,whose goal is to reduce disaster costs and to improve economic resilience.
文摘Objective: To describe the demographic and evolutionary characteristics of pregnant and postpartum women with coronavirus disease 2019 (COVID-19) admitted to a medium-sized hospital in Brazil. Method: This is a descriptive and retrospective study, collected from medical records, from March 2020 to October 2021 in a hospital located in Cuiabá (MT). Results: Pregnant and puerperal women with COVID-19 who needed hospitalization were mixed-race, from the metropolitan area, and carriers of moderate and severe forms of the disease. The primary risk condition found was overweight/obesity, and pre-gestational diabetes, hypertension, asthma, and autoimmune disease were the most prevalent comorbidities in the group. Elevated lactate dehydrogenase (LDH), c-reactive protein (CRP), and D-dimer were relevant laboratory findings in this group of patients. The most frequent maternal outcomes were respiratory failure, invasive ventilatory support, thromboembolic phenomena, sepsis, and preterm labor. Maternal death occurred in 6.4% of pregnant women. Most maternal deaths were of women who lived in the interior of the state, and the minority arrived on adequate ventilatory support. Prematurity and the need for neonatal intensive care unit (NICU) were significant complications for neonates. Stillbirth/neonatal mortality occurred in 11.0%. Conclusion: The clinical conditions at hospitalization were associated with worse living conditions and lack of access to health care, resulting in increased chances of severity and worsening outcomes in this group of women and neonates.
