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1.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-306095

ABSTRACT

Recently there has been a formidable work which has been put up from the people who are working in the frontlines such as hospitals, clinics, and labs alongside researchers and scientists who are also putting tremendous efforts in the fight against COVID-19 pandemic. Due to the preposterous spread of the virus, the integration of the artificial intelligence has taken a considerable part in the health sector, by implementing the fundamentals of Automatic Speech Recognition (ASR) and deep learning algorithms. In this paper, we illustrate the importance of speech signal processing in the extraction of the Mel-Frequency Cepstral Coefficients (MFCCs) of the COVID-19 and non-COVID-19 samples and find their relationship using Pearson correlation coefficients. Our results show high similarity in MFCCs between different COVID-19 cough and breathing sounds, while MFCC of voice is more robust between COVID-19 and non-COVID-19 samples. Moreover, our results are preliminary, and there is a possibility to exclude the voices of COVID-19 patients from further processing in diagnosing the disease.

2.
Malar J ; 19(1): 457, 2020 Dec 23.
Article in English | MEDLINE | ID: covidwho-992487

ABSTRACT

The incidence and mortality of COVID-19, according to the World Health Organization reports, shows a noticeable difference between North America, Western Europe, and South Asia on one hand and most African countries on the other hand, especially the malaria-endemic countries. Although this observation could be attributed to limited testing capacity, mitigation tools adopted and cultural habits, many theories have been postulated to explain this difference in prevalence and mortality. Because death tends to occur more in elders, both the role of demography, and how the age structure of a population may contribute to the difference in mortality rate between countries were discussed. The variable distribution of the ACEI/D and the ACE2 (C1173T substitution) polymorphisms has been postulated to explain this variable prevalence. Up-to-date data regarding the role of hydroxychloroquine (HCQ) and chloroquine (CQ) in COVID-19 have been summarized. The article also sheds lights on how the similarity of malaria and COVID-19 symptoms can lead to misdiagnosis of one disease for the other or overlooking the possibility of co-infection. As the COVID-19 pandemic threatens the delivery of malaria services, such as the distribution of insecticide-treated nets (ITNs), indoor residual spraying, as well as malaria chemoprevention there is an urgent need for rapid and effective responses to avoid malaria outbreaks.


Subject(s)
COVID-19/epidemiology , Malaria/epidemiology , SARS-CoV-2 , Age Factors , Angiotensin-Converting Enzyme 2/physiology , COVID-19/diagnosis , COVID-19/drug therapy , Chloroquine/therapeutic use , Humans , Hydroxychloroquine/therapeutic use , Interferons/physiology , Malaria/diagnosis , Malaria/drug therapy , Prevalence
3.
SSRN; 2020.
Preprint | SSRN | ID: ppcovidwho-1406

ABSTRACT

Background: Although diabetes has been associated with COVID-19 mortality, the absolute and relative risks for Type 1 and Type 2 diabetes are unknown.brbrMe

4.
Lancet Diabetes Endocrinol ; 8(10): 813-822, 2020 10.
Article in English | MEDLINE | ID: covidwho-712030

ABSTRACT

BACKGROUND: Although diabetes has been associated with COVID-19-related mortality, the absolute and relative risks for type 1 and type 2 diabetes are unknown. We assessed the independent effects of diabetes status, by type, on in-hospital death in England in patients with COVID-19 during the period from March 1 to May 11, 2020. METHODS: We did a whole-population study assessing risks of in-hospital death with COVID-19 between March 1 and May 11, 2020. We included all individuals registered with a general practice in England who were alive on Feb 16, 2020. We used multivariable logistic regression to examine the effect of diabetes status, by type, on in-hospital death with COVID-19, adjusting for demographic factors and cardiovascular comorbidities. Because of the absence of data on total numbers of people infected with COVID-19 during the observation period, we calculated mortality rates for the population as a whole, rather than the population who were infected. FINDINGS: Of the 61 414 470 individuals who were alive and registered with a general practice on Feb 16, 2020, 263 830 (0·4%) had a recorded diagnosis of type 1 diabetes, 2 864 670 (4·7%) had a diagnosis of type 2 diabetes, 41 750 (0·1%) had other types of diabetes, and 58 244 220 (94·8%) had no diabetes. 23 698 in-hospital COVID-19-related deaths occurred during the study period. A third occurred in people with diabetes: 7434 (31·4%) in people with type 2 diabetes, 364 (1·5%) in those with type 1 diabetes, and 69 (0·3%) in people with other types of diabetes. Unadjusted mortality rates per 100 000 people over the 72-day period were 27 (95% CI 27-28) for those without diabetes, 138 (124-153) for those with type 1 diabetes, and 260 (254-265) for those with type 2 diabetes. Adjusted for age, sex, deprivation, ethnicity, and geographical region, compared with people without diabetes, the odds ratios (ORs) for in-hospital COVID-19-related death were 3·51 (95% CI 3·16-3·90) in people with type 1 diabetes and 2·03 (1·97-2·09) in people with type 2 diabetes. These effects were attenuated to ORs of 2·86 (2·58-3·18) for type 1 diabetes and 1·80 (1·75-1·86) for type 2 diabetes when also adjusted for previous hospital admissions with coronary heart disease, cerebrovascular disease, or heart failure. INTERPRETATION: The results of this nationwide analysis in England show that type 1 and type 2 diabetes were both independently associated with a significant increased odds of in-hospital death with COVID-19. FUNDING: None.


Subject(s)
Betacoronavirus , Coronavirus Infections/mortality , Diabetes Mellitus, Type 1/mortality , Diabetes Mellitus, Type 2/mortality , Hospital Mortality/trends , Pneumonia, Viral/mortality , Population Surveillance , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19 , Child , Child, Preschool , Comorbidity , Coronavirus Infections/diagnosis , Diabetes Mellitus, Type 1/diagnosis , Diabetes Mellitus, Type 2/diagnosis , England/epidemiology , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , Mortality/trends , Pandemics , Pneumonia, Viral/diagnosis , Population Surveillance/methods , SARS-CoV-2 , Young Adult
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