Tailoring time series models for forecasting coronavirus spread: Case studies of 187 countries

Abstract

When will the coronavirus end? Are the current precautionary measures effective? To answer these questions it is important to forecast regularly and accurately the spread of COVID-19 infections. Different time series forecasting models have been applied in the literature to tackle the pandemic situation. The current research efforts developed few of these models and validates its accuracy for selected countries. It becomes difficult to draw an objective comparison between the performance of these models at a global scale. This is because, the time series trend for the infection differs between the countries depending on the strategies adopted by the healthcare organizations to decrease the spread. Consequently, it is important to develop a tailored model for a country that allows healthcare organizations to better judge the effect of the undertaken precautionary measures, and provision more efficiently the needed resources to face this disease. This paper addresses this void. We develop and compare the performance of the time series models in the literature in terms of root mean squared error and mean absolute percentage error.

Systematic review of the effects of pandemic confinements on body weight and their determinants

“…Physical activity before lockdown (35) Decreased physical activity (3)(4)(5)7,8,12,15,16,(19)(20)(21)(22)24,26,(29)(30)(31)33,(35)(36)(37)(38)(39) Limitations of outdoor and in-gym activities (2,4,5,7) Increased screen/tv time (7,12,21,31,33,36,39) Comorbidities Associated chronic illness (1,33,(39)(40)(41) Determinants that can be associated with weight loss…”

“…Devastating physical morbidity and mortality outcomes due to coronavirus disease 2019 have been mitigated by (1,2) social distancing and quarantine measures (3) , with significant direct and indirect health implications. Although lockdown has reduced the "R number", physical wellbeing may have suffered from increased levels of stress, anxiety, and mental health issues (4)(5)(6) .…”

“…( 2 0 2 0 ) C O V I D -1 9 Q u a r a n t i n e -R e l a t e d M e n t a l H e a l t h S y m p t o m s a n d t h e i r C o r r e l a t e s a m o n g M o t h e r s : A C r o s s S e c t R u i s s e n M M , R e g e e r H , L a n d s t r a C P , e t a l . ( 2 0 2 1 ) I n c r e a s e d s t r e s s , w e i g h t g a i n a n d l e s s e x e r c i s e i n r e l a t i o n t o g l y c e m i c c o n t r o l i n p e o p l e w i t h t y p e 1 Female (1)(2)(3)(4)(5)(6) Baseline obese and overweight (2,3,(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) BMI <24 (16,17) Age group > 45 years (3,9,18) Age group < 25 years (1,9) Having children under the age of 18 at home (1) Changed work environment to working from home (1,2,19) Work Environment…”

“…Increased food consumption (3,4,7,12,14,(20)(21)(22) Decreased consumption of fresh food products (particularly fruits, vegetables, and fish) (2,3,12,16,23) Increased consumption of homemade recipes, sweets, and pizza (7,8) Increased home cooking (3) Increased cereal consumption (2,7) Consumption of unhealthy foods (2)(3)(4)(5)7,7,8,10,12,24,25) Poor attention to diet balance (7) Snacking after dinner (2,26) Binge eating (24,27) Loss of control to eating (24) Eating in response to stress as a coping mechanism (23,26) Eating secondary to appearance and smell of food (26) Emotional eating (5,11,23) Increase in alcohol intake (3,4,12) Psychological factors…”

Clusters in the Spread of the COVID-19 Pandemic: Evidence From the G20 Countries

“…Besides, the neighboring countries have significant effects on the spread of COVID-19. Ismail et al ( 7 ) confirm the evidence of convergence for the indicators on the spread of COVID-19 in 187 countries.…”