Global burden of respiratory infections associated with seasonal influenza in children under 5 years in 2018: a systematic review and modelling study

Background: Seasonal influenza virus is a common cause of acute lower respiratory infection (ALRI) in young children. In 2008, we estimated that 20 million influenza-virus-associated ALRI and 1 million influenza-virus-associated severe ALRI occurred in children under 5 years globally. Despite this substantial burden, only a few low-income and middle-income countries have adopted routine influenza vaccination policies for children and, where present, these have achieved only low or unknown levels of vaccine uptake. Moreover, the influenza burden might have changed due to the emergence and circulation of influenza A/H1N1pdm09. We aimed to incorporate new data to update estimates of the global number of cases, hospital admissions, and mortality from influenza-virus-associated respiratory infections in children under 5 years in 2018. Methods: We estimated the regional and global burden of influenza-associated respiratory infections in children under 5 years from a systematic review of 100 studies published between Jan 1, 1995, and Dec 31, 2018, and a further 57 high-quality unpublished studies. We adapted the Newcastle-Ottawa Scale to assess the risk of bias. We estimated incidence and hospitalisation rates of influenza-virus-associated respiratory infections by severity, case ascertainment, region, and age. We estimated in-hospital deaths from influenza virus ALRI by combining hospital admissions and in-hospital case-fatality ratios of influenza virus ALRI. We estimated the upper bound of influenza virus-associated ALRI deaths based on the number of in-hospital deaths, US paediatric influenza-associated death data, and population-based childhood all-cause pneumonia mortality data in six sites in low-income and lower-middle-income countries. Findings: In 2018, among children under 5 years globally, there were an estimated 109·5 million influenza virus episodes (uncertainty range [UR] 63·1–190·6), 10·1 million influenza-virus-associated ALRI cases (6·8–15·1); 870 000 influenza-virus-associated ALRI hospital admissions (543 000–1 415 000), 15 300 in-hospital deaths (5800–43 800), and up to 34 800 (13 200–97 200) overall influenza-virus-associated ALRI deaths. Influenza virus accounted for 7% of ALRI cases, 5% of ALRI hospital admissions, and 4% of ALRI deaths in children under 5 years. About 23% of the hospital admissions and 36% of the in-hospital deaths were in infants under 6 months. About 82% of the in-hospital deaths occurred in low-income and lower-middle-income countries. Interpretation: A large proportion of the influenza-associated burden occurs among young infants and in low-income and lower middle-income countries. Our findings provide new and important evidence for maternal and paediatric influenza immunisation, and should inform future immunisation policy particularly in low-income and middle-income countries. Funding: WHO; Bill & Melinda Gates Foundation.Fil: Wang, Xin. University of Edinburgh; Reino UnidoFil: Li, You. University of Edinburgh; Reino UnidoFil: O'Brien, Katherine L.. University Johns Hopkins; Estados UnidosFil: Madhi, Shabir A.. University of the Witwatersrand; SudáfricaFil: Widdowson, Marc Alain. Centers for Disease Control and Prevention; Estados UnidosFil: Byass, Peter. Umea University; SueciaFil: Omer, Saad B.. Yale School Of Public Health; Estados UnidosFil: Abbas, Qalab. Aga Khan University; PakistánFil: Ali, Asad. Aga Khan University; PakistánFil: Amu, Alberta. Dodowa Health Research Centre; GhanaFil: Azziz-Baumgartner, Eduardo. Centers for Disease Control and Prevention; Estados UnidosFil: Bassat, Quique. University Of Barcelona; EspañaFil: Abdullah Brooks, W.. University Johns Hopkins; Estados UnidosFil: Chaves, Sandra S.. Centers for Disease Control and Prevention; Estados UnidosFil: Chung, Alexandria. University of Edinburgh; Reino UnidoFil: Cohen, Cheryl. National Institute For Communicable Diseases; SudáfricaFil: Echavarría, Marcela Silvia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas "Norberto Quirno". CEMIC-CONICET; ArgentinaFil: Fasce, Rodrigo A.. Public Health Institute; ChileFil: Gentile, Angela. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutiérrez"; ArgentinaFil: Gordon, Aubree. University of Michigan; Estados UnidosFil: Groome, Michelle. University of the Witwatersrand; SudáfricaFil: Heikkinen, Terho. University Of Turku; FinlandiaFil: Hirve, Siddhivinayak. Kem Hospital Research Centre; IndiaFil: Jara, Jorge H.. Universidad del Valle de Guatemala; GuatemalaFil: Katz, Mark A.. Clalit Research Institute; IsraelFil: Khuri Bulos, Najwa. University Of Jordan School Of Medicine; JordaniaFil: Krishnan, Anand. All India Institute Of Medical Sciences; IndiaFil: de Leon, Oscar. Universidad del Valle de Guatemala; GuatemalaFil: Lucero, Marilla G.. Research Institute For Tropical Medicine; FilipinasFil: McCracken, John P.. Universidad del Valle de Guatemala; GuatemalaFil: Mira-Iglesias, Ainara. Fundación Para El Fomento de la Investigación Sanitaria; EspañaFil: Moïsi, Jennifer C.. Agence de Médecine Préventive; FranciaFil: Munywoki, Patrick K.. No especifíca;Fil: Ourohiré, Millogo. No especifíca;Fil: Polack, Fernando Pedro. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Rahi, Manveer. University of Edinburgh; Reino UnidoFil: Rasmussen, Zeba A.. National Institutes Of Health; Estados UnidosFil: Rath, Barbara A.. Vienna Vaccine Safety Initiative; AlemaniaFil: Saha, Samir K.. Child Health Research Foundation; BangladeshFil: Simões, Eric A.F.. University of Colorado; Estados UnidosFil: Sotomayor, Viviana. Ministerio de Salud de Santiago de Chile; ChileFil: Thamthitiwat, Somsak. Thailand Ministry Of Public Health; TailandiaFil: Treurnicht, Florette K.. University of the Witwatersrand; SudáfricaFil: Wamukoya, Marylene. African Population & Health Research Center; KeniaFil: Lay-Myint, Yoshida. Nagasaki University; JapónFil: Zar, Heather J.. University of Cape Town; SudáfricaFil: Campbell, Harry. University of Edinburgh; Reino UnidoFil: Nair, Harish. University of Edinburgh; Reino Unid

Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning

Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL

An Efficient Surface Map Creation and Tracking Using Smartphone Sensors and Crowdsourcing

Like Smart Home and Smart Devices, Smart Navigation has become necessary to travel through the congestion of the structure of either building or in the wild. The advancement in smartphone technology and incorporation of many different precise sensors have made the smartphone a unique choice for developing practical navigation applications. Many have taken the initiative to address this by developing mobile-based solutions. Here, a cloud-based intelligent traveler assistant is proposed that exploits user-generated position and elevation data collected from ubiquitous smartphone devices equipped with Accelerometer, Gyroscope, Magnetometer, and GPS (Global Positioning System) sensors. The data can be collected by the pedestrians and the drivers, and are then automatically put into topological information. The platform and associated innovative application allow travelers to create a map of a route or an infrastructure with ease and to share the information for others to follow. The cloud-based solution that does not cost travelers anything allows them to create, access, and follow any maps online and offline. The proposed solution consumes little battery power and can be used with lowly configured resources. The ability to create unknown, unreached, or unrecognized rural/urban road maps, building structures, and the wild map with the help of volunteer traveler-generated data and to share these data with the greater community makes the presented solution unique and valuable. The proposed crowdsourcing method of knowing the unknown would be an excellent support for travelers

Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning

Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL

Global burden of respiratory infections associated with seasonal influenza in children under 5 years in 2018: a systematic review and modelling study

BACKGROUND: Seasonal influenza virus is a common cause of acute lower respiratory infection (ALRI) in young children. In 2008, we estimated that 20 million influenza-virus-associated ALRI and 1 million influenza-virus-associated severe ALRI occurred in children under 5 years globally. Despite this substantial burden, only a few low-income and middle-income countries have adopted routine influenza vaccination policies for children and, where present, these have achieved only low or unknown levels of vaccine uptake. Moreover, the influenza burden might have changed due to the emergence and circulation of influenza A/H1N1pdm09. We aimed to incorporate new data to update estimates of the global number of cases, hospital admissions, and mortality from influenza-virus-associated respiratory infections in children under 5 years in 2018. METHODS: We estimated the regional and global burden of influenza-associated respiratory infections in children under 5 years from a systematic review of 100 studies published between Jan 1, 1995, and Dec 31, 2018, and a further 57 high-quality unpublished studies. We adapted the Newcastle-Ottawa Scale to assess the risk of bias. We estimated incidence and hospitalisation rates of influenza-virus-associated respiratory infections by severity, case ascertainment, region, and age. We estimated in-hospital deaths from influenza virus ALRI by combining hospital admissions and in-hospital case-fatality ratios of influenza virus ALRI. We estimated the upper bound of influenza virus-associated ALRI deaths based on the number of in-hospital deaths, US paediatric influenza-associated death data, and population-based childhood all-cause pneumonia mortality data in six sites in low-income and lower-middle-income countries. FINDINGS: In 2018, among children under 5 years globally, there were an estimated 109·5 million influenza virus episodes (uncertainty range [UR] 63·1–190·6), 10·1 million influenza-virus-associated ALRI cases (6·8–15·1); 870 000 influenza-virus-associated ALRI hospital admissions (543 000–1 415 000), 15 300 in-hospital deaths (5800–43 800), and up to 34 800 (13 200–97 200) overall influenza-virus-associated ALRI deaths. Influenza virus accounted for 7% of ALRI cases, 5% of ALRI hospital admissions, and 4% of ALRI deaths in children under 5 years. About 23% of the hospital admissions and 36% of the in-hospital deaths were in infants under 6 months. About 82% of the in-hospital deaths occurred in low-income and lower-middle-income countries. INTERPRETATION: A large proportion of the influenza-associated burden occurs among young infants and in low-income and lower middle-income countries. Our findings provide new and important evidence for maternal and paediatric influenza immunisation, and should inform future immunisation policy particularly in low-income and middle-income countries