scholarly journals Mining Social Media to Identify Heat Waves

2019 ◽  
Vol 16 (5) ◽  
pp. 762 ◽  
Author(s):  
Francesca Cecinati ◽  
Tom Matthews ◽  
Sukumar Natarajan ◽  
Nick McCullen ◽  
David Coley
Keyword(s):  
Social Media ◽  
Heat Wave ◽  
Excess Mortality ◽  
Heat Waves ◽  
Mortality Data ◽  
Climatic Data ◽  
Quantitative Definition ◽  
Related Mortality ◽  
Or In Developing Countries ◽  
Better Than

Heat waves are one of the deadliest of natural hazards and their frequency and intensity will likely increase as the climate continues to warm. A challenge in studying these phenomena is the lack of a universally accepted quantitative definition that captures both temperature anomalies and associated mortality. We test the hypothesis that social media mining can be used to identify heat wave mortality. Applying the approach to India, we find that the number of heat-related tweets correlates with heat-related mortality much better than traditional climate-based indicators, especially at larger scales, which identify many heat wave days that do not lead to excess mortality. We conclude that social media based heat wave identification can complement climatic data and can be used to: (1) study heat wave impacts at large scales or in developing countries, where mortality data are difficult to obtain and uncertain, and (2) to track dangerous heat wave events in real time.

scholarly journals The impact of the 2003 heat wave on daily mortality in England and Wales and the use of rapid weekly mortality estimates

2005 ◽  
Vol 10 (7) ◽  
pp. 15-16 ◽  
Author(s):  
H Johnson ◽  
S Kovats ◽  
G McGregor ◽  
J Stedman ◽  
M Gibbs ◽  
...  
Keyword(s):  
Heat Wave ◽  
Excess Mortality ◽  
Heat Waves ◽  
Mortality Data ◽  
Daily Mortality ◽  
England And Wales ◽  
National Statistics ◽  
The United Kingdom ◽  
The Impact ◽  
Mortality Estimates

This paper describes a retrospective analysis of the impact of the 2003 heat wave on mortality in England and Wales, and compares this with rapid estimates based on the Office for National Statistics routine weekly deaths reporting system. Daily mortality data for 4 to 13 August 2003, when temperatures were much hotter than normally seen in England, were compared with averages for the same period in years 1998 to 2002. The August 2003 heat wave was associated with a large short-term increase in mortality, particularly in London. Ozone and particulate matter concentrations were also elevated during the heat wave. Overall, there were 2139 (16%) excess deaths in England and Wales. Worst affected were people over the age of 75 years. The impact was greatest in the London region where deaths in those over the age of 75 increased by 59%. Estimated excess mortality was greater than for other recent heat waves in the United Kingdom. The estimated number of deaths registered each week is reported by the Office for National Statistics. The first clear indication of a substantial increase in deaths was published on 21 August 2003. This provided a quick first estimate of the number of deaths attributable to the heat wave and reflected the pattern of daily deaths in relation to the hottest days, but underestimated the excess when compared with the later analysis.

The Impact of Heat Waves on Mortality in Nanjing Area from 2004 to 2010

2013 ◽  
Vol 726-731 ◽  
pp. 931-935
Author(s):  
Yuan Shu Jing ◽  
Di Zhang ◽  
Min Fei Yan ◽  
Jian Guo Tan
Keyword(s):  
Heat Wave ◽  
Excess Mortality ◽  
Heat Waves ◽  
Residual Disease ◽  
Meteorological Data ◽  
Age Groups ◽  
Maximum Temperature ◽  
Mortality Data ◽  
Average Maximum ◽  
The Impact

This paper analyzed the excess mortality change in nine districts of Nanjing city, based on mortality data and meteorological data from 2004 to 2010. Taken a typical heat waves process in summer of 2006 as an example, it was discussed of the effect of the heat process on different gender, different age groups , and various disease death toll and excess mortality changes. The excess mortality was associated with the average maximum temperature and average minimum temperature during the heat waves. Excess mortality occurred in the middle of June heat wave when excess mortality was much higher than in other time periods. In late June, early July to early August, the excess mortality is relatively small. The average daily deaths are increasing with increasing age for male and female, and every age death numbers is higher than that with no heat waves during the heat wave period. In addition to the respiratory system diseases, diseases of the genitourinary system, other diseases, residual disease in the heat waves has increased, and diseases of the nervous system and the endocrine system diseases of excess mortality rate reached a staggering 342.93% and 119.63%, accounting for almost half of the total heat excess mortality. The heat waves effect is very obvious. The conclusion is of great significance for prevention of high temperature heat harm.

scholarly journals Excess Mortality in Istanbul during Extreme Heat Waves between 2013 and 2017

2019 ◽  
Vol 16 (22) ◽  
pp. 4348 ◽  
Author(s):  
Günay Can ◽  
Ümit Şahin ◽  
Uğurcan Sayılı ◽  
Marjolaine Dubé ◽  
Beril Kara ◽  
...  
Keyword(s):  
Heat Wave ◽  
Excess Mortality ◽  
Intervention Studies ◽  
Heat Waves ◽  
Epidemiological Studies ◽  
Extreme Heat ◽  
Mortality And Morbidity ◽  
Climate Simulations ◽  
Morbidity Risks ◽  
Excess Deaths

Heat waves are one of the most common direct impacts of anthropogenic climate change and excess mortality their most apparent impact. While Turkey has experienced an increase in heat wave episodes between 1971 and 2016, no epidemiological studies have examined their potential impacts on public health so far. In this study excess mortality in Istanbul attributable to extreme heat wave episodes between 2013 and 2017 is presented. Total excess deaths were calculated using mortality rates across different categories, including age, sex, and cause of death. The analysis shows that three extreme heat waves in the summer months of 2015, 2016, and 2017, which covered 14 days in total, significantly increased the mortality rate and caused 419 excess deaths in 23 days of exposure. As climate simulations show that Turkey is one of the most vulnerable countries in the Europe region to the increased intensity of heat waves until the end of the 21st century, further studies about increased mortality and morbidity risks due to heat waves in Istanbul and other cities, as well as intervention studies, are necessary.

scholarly journals Public crowdsensing of heat waves by social media data

2017 ◽  
Vol 14 ◽  
pp. 217-226 ◽  
Author(s):  
Valentina Grasso ◽  
Alfonso Crisci ◽  
Marco Morabito ◽  
Paolo Nesi ◽  
Gianni Pantaleo
Keyword(s):  
Social Media ◽  
Heat Wave ◽  
Urban Areas ◽  
Heat Waves ◽  
Local Level ◽  
Climatic Conditions ◽  
World Health ◽  
Wave Impact ◽  
Recent Climate ◽  
The Impact

Abstract. Investigating on society-related heat wave hazards is a global issue concerning the people health. In the last two decades, Europe experienced several severe heat wave episodes with catastrophic effects in term of human mortality (2003, 2010 and 2015). Recent climate investigations confirm that this threat will represent a key issue for the resiliency of urban communities in next decades. Several important mitigation actions (Heat-Health Action Plans) against heat hazards have been already implemented in some WHO (World Health Organization) European region member states to encourage preparedness and response to extreme heat events. Nowadays, social media (SM) offer new opportunities to indirectly measure the impact of heat waves on society. Using the crowdsensing concept, a micro-blogging platform like Twitter may be used as a distributed network of mobile sensors that react to external events by exchanging messages (tweets). This work presents a preliminary analysis of tweets related to heat waves that occurred in Italy in summer 2015. Using TwitterVigilance dashboard, developed by the University of Florence, a sample of tweets related to heat conditions was retrieved, stored and analyzed for main features. Significant associations between the daily increase in tweets and extreme temperatures were presented. The daily volume of Twitter users and messages revealed to be a valuable indicator of heat wave impact at the local level, in urban areas. Furthermore, with the help of Generalized Additive Model (GAM), the volume of tweets in certain locations has been used to estimate thresholds of local discomfort conditions. These city-specific thresholds are the result of dissimilar climatic conditions and risk cultures.

scholarly journals Mortality in Portugal associated with the heat wave of August 2003: Early estimation of effect, using a rapid method

2005 ◽  
Vol 10 (7) ◽  
pp. 5-6 ◽  
Author(s):  
P J Nogueira ◽  
J M Falcão ◽  
M T Contreiras ◽  
E Paixão ◽  
J Brandão ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Rapid Method ◽  
Heat Wave ◽  
Surveillance System ◽  
Excess Mortality ◽  
Heat Waves ◽  
Preliminary Estimate ◽  
Potential Influence ◽  
Daily Number ◽  
Mainland Portugal

During the first two weeks of August 2003, Portugal was affected by a severe heat wave. Following the identification in Portugal of the influence of heat waves on mortality in 1981 and 1991 (estimated excess of about 1900 and 1000 deaths respectively), the Observatório Nacional de Saúde (ONSA) - Instituto Nacional de Saúde Dr. Ricardo Jorge, together with the Vigilância Previsão e Informação - Instituto de Meteorologia, created a surveillance system called ÍCARO, which has been in operation since 1999. ÍCARO identifies heat waves with potential influence on mortality [1]. Before the end of the 2003 heat wave, ONSA had produced a preliminary estimate of its effect on mortality. The results based on daily number of deaths from 1 June to 12 August 2003 were presented within 4 working days. Data was gathered from 31 National Civil registrars, covering the district capitals of all 18 districts of mainland Portugal, and representing approximately 40% of the mainland’s mortality. The number of deaths registered in the period 30 July to 12 August was compared with the ones registered during 3 comparison periods: (in July): 1-14 July, 1-28 July, and 15-28 July). 15-28 July, the period best resembling the heat wave in time and characteristics, produced an estimation of 37.7% higher mortality rate then the value expected under normal temperature conditions. From this value, an estimate of 1316 death excess was obtained for mainland Portugal. The main purpose of this article is to present the method used to identify and assess the occurrence of an effect (excess mortality) during the heat wave of summer 2003.

A simple method for estimating excess mortality due to heat waves, as applied to the 2006 California heat wave

2009 ◽  
Vol 55 (2) ◽  
pp. 133-137 ◽  
Author(s):  
Sumi Hoshiko ◽  
Paul English ◽  
Daniel Smith ◽  
Roger Trent
Keyword(s):  
Heat Wave ◽  
Excess Mortality ◽  
Heat Waves ◽  
Simple Method

scholarly journals Local mortality estimates during the COVID-19 pandemic in Italy

2021 ◽  
Author(s):  
Augusto Cerqua ◽  
Roberta Di Stefano ◽  
Marco Letta ◽  
Sara Miccoli
Keyword(s):  
Machine Learning ◽  
Excess Mortality ◽  
Control Method ◽  
Local Level ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Mortality Data ◽  
Official Method ◽  
Learning Techniques ◽  
Mortality Estimates

AbstractEstimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

scholarly journals Impact of Relaxing Covid-19 Social Distancing Measures on Rural North Wales: A Simulation Analysis

2020 ◽  
Vol 8 ◽  
Author(s):  
Rhodri P. Hughes ◽  
Dyfrig A. Hughes
Keyword(s):  
Simulation Analysis ◽  
Model Averaging ◽  
Incidence Rates ◽  
Mortality Data ◽  
Rural Regions ◽  
Social Distancing ◽  
North Wales ◽  
The Uk ◽  
Sigmoidal Growth ◽  
Related Mortality

Background: Social distancing policies aimed to limit Covid-19 across the UK were gradually relaxed between May and August 2020, as peak incidences passed. Population density is an important driver of national incidence rates; however peak incidences in rural regions may lag national figures by several weeks. We aimed to forecast the timing of peak Covid-19 mortality rate in rural North Wales.Methods: Covid-19 related mortality data up to 7/5/2020 were obtained from Public Health Wales and the UK Government. Sigmoidal growth functions were fitted by non-linear least squares and model averaging used to extrapolate mortality to 24/8/2020. The dates of peak mortality incidences for North Wales, Wales and the UK; and the percentage of predicted mortality at 24/8/2020 were calculated.Results: The peak daily death rates in Wales and the UK were estimated to have occurred on the 14/04/2020 and 15/04/2020, respectively. For North Wales, this occurred on the 07/05/2020, corresponding to the date of analysis. The number of deaths reported in North Wales on 07/05/2020 represents 33% of the number predicted to occur by 24/08/2020, compared with 74 and 62% for Wales and the UK, respectively.Conclusion: Policies governing the movement of people in the gradual release from lockdown are likely to impact significantly on areas–principally rural in nature–where cases of Covid-19, deaths and immunity are likely to be much lower than in populated areas. This is particularly difficult to manage across jurisdictions, such as between England and Wales, and in popular holiday destinations.

scholarly journals A heat-health watch and warning system with extended season and evolving thresholds

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Mahamat Abdelkerim Issa ◽  
Fateh Chebana ◽  
Pierre Masselot ◽  
Céline Campagna ◽  
Éric Lavigne ◽  
...  
Keyword(s):  
Excess Mortality ◽  
Heat Waves ◽  
Meteorological Data ◽  
Warm Season ◽  
Warning System ◽  
Early Warning Systems ◽  
Climate Projections ◽  
Warning Systems ◽  
Extreme Heat Events ◽  
Watch And Warning System

Abstract Background Many countries have developed heat-health watch and warning systems (HHWWS) or early-warning systems to mitigate the health consequences of extreme heat events. HHWWS usually focuses on the four hottest months of the year and imposes the same threshold over these months. However, according to climate projections, the warm season is expected to extend and/or shift. Some studies demonstrated that health impacts of heat waves are more severe when the human body is not acclimatized to the heat. In order to adapt those systems to potential heat waves occurring outside the hottest months of the season, this study proposes specific health-based monthly heat indicators and thresholds over an extended season from April to October in the northern hemisphere. Methods The proposed approach, an adoption and extension of the HHWWS methodology currently implemented in Quebec (Canada). The latter is developed and applied to the Greater Montreal area (current population 4.3 million) based on historical health and meteorological data over the years. This approach consists of determining excess mortality episodes and then choosing monthly indicators and thresholds that may involve excess mortality. Results We obtain thresholds for the maximum and minimum temperature couple (in °C) that range from (respectively, 23 and 12) in April, to (32 and 21) in July and back to (25 and 13) in October. The resulting HHWWS is flexible, with health-related thresholds taking into account the seasonality and the monthly variability of temperatures over an extended summer season. Conclusions This adaptive and more realistic system has the potential to prevent, by data-driven health alerts, heat-related mortality outside the typical July–August months of heat waves. The proposed methodology is general and can be applied to other regions and situations based on their characteristics.

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