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Abstract
Infectious diseases like COVID-19 spread rapidly and have led to substantial economic
loss worldwide, including in Pakistan. The effect of weather on COVID-19 spreading
needs more detailed examination, as some studies have claimed to mitigate its spread.
COVID-19 was declared a pandemic by WHO and has been reported in about 210 countries
worldwide, including Asia, Europe, the USA, and North America. Person-to-person contact
and international air travel between the nations were the leading causes behind the
spreading of SARS-CoV-2 from its point of origin, besides the natural forces. However,
further spread and infection within the community or country can be aided by natural
elements, such as the weather. Therefore, the correlation between COVID-19 and temperature
can be better elucidated in countries like Pakistan, where SARS-CoV-2 has affected
at least 0.37 million people. This study collected Pakistan’s COVID-19 infection and
mortality data for ten months (March–December 2020). Related weather parameters, temperature,
and humidity were also obtained for the same course of time. The collected data were
processed and used to compare the performance of various time series prediction models
in terms of mean squared error (MSE), root-mean-squared error (RMSE), and mean absolute
percentage error (MAPE). This paper, using the time series model, estimates the effect
of humidity, temperature, and other weather parameters on COVID-19 transmission by
obtaining the correlation among the total infected cases and the number of deaths
and weather variables in a particular region. Results depict that weather parameters
hold more influence in evaluating the sum number of cases and deaths than other factors
like community, age, and the total population. Therefore, temperature and humidity
are salient parameters for predicting COVID-19 affected instances. Moreover, it is
concluded that the higher the temperature, the lesser the mortality due to COVID-19
infection.
Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
The city of Wuhan in China is the focus of global attention due to an outbreak of a febrile respiratory illness due to a coronavirus 2019-nCoV. In December 2019, there was an outbreak of pneumonia of unknown cause in Wuhan, Hubei province in China, with an epidemiological link to the Huanan Seafood Wholesale Market where there was also sale of live animals. Notification of the WHO on 31 Dec 2019 by the Chinese Health Authorities has prompted health authorities in Hong Kong, Macau, and Taiwan to step up border surveillance, and generated concern and fears that it could mark the emergence of a novel and serious threat to public health (WHO, 2020a, Parr, 2020). The Chinese health authorities have taken prompt public health measures including intensive surveillance, epidemiological investigations, and closure of the market on 1 Jan 2020. SARS-CoV, MERS-CoV, avian influenza, influenza and other common respiratory viruses were ruled out. The Chinese scientists were able to isolate a 2019-nCoV from a patient within a short time on 7 Jan 2020 and perform genome sequencing of the 2019-nCoV. The genetic sequence of the 2019-nCoV has become available to the WHO on 12 Jan 2020 and this has facilitated the laboratories in different countries to produce specific diagnostic PCR tests for detecting the novel infection (WHO, 2020b). The 2019-nCoV is a β CoV of group 2B with at least 70% similarity in genetic sequence to SARS-CoV and has been named 2019-nCoV by the WHO. SARS is a zoonosis caused by SARS-CoV, which first emerged in China in 2002 before spreading to 29 countries/regions in 2003 through a travel-related global outbreak with 8,098 cases with a case fatality rate of 9.6%. Nosocomial transmission of SARS-CoV was common while the primary reservoir was putatively bats, although unproven as the actual source and the intermediary source was civet cats in the wet markets in Guangdong (Hui and Zumla, 2019). MERS is a novel lethal zoonotic disease of humans endemic to the Middle East, caused by MERS-CoV. Humans are thought to acquire MERS-CoV infection though contact with camels or camel products with a case fatality rate close to 35% while nosocomial transmission is also a hallmark (Azhar et al., 2019). The recent outbreak of clusters of viral pneumonia due to a 2019-nCoV in the Wuhan market poses significant threats to international health and may be related to sale of bush meat derived from wild or captive sources at the seafood market. As of 10 Jan 2020, 41 patients have been diagnosed to have infection by the 2019-nCoV animals. The onset of illness of the 41 cases ranges from 8 December 2019 to 2 January 2020. Symptoms include fever (>90% cases), malaise, dry cough (80%), shortness of breath (20%) and respiratory distress (15%). The vital signs were stable in most of the cases while leucopenia and lymphopenia were common. Among the 41 cases, six patients have been discharged, seven patients are in critical care and one died, while the remaining patients are in stable condition. The fatal case involved a 61 year-old man with an abdominal tumour and cirrhosis who was admitted to a hospital due to respiratory failure and severe pneumonia. The diagnoses included severe pneumonia, acute respiratory distress syndrome, septic shock and multi-organ failure. The 2019-nCoV infection in Wuhan appears clinically milder than SARS or MERS overall in terms of severity, case fatality rate and transmissibility, which increases the risk of cases remaining undetected. There is currently no clear evidence of human to human transmission. At present, 739 close contacts including 419 healthcare workers are being quarantined and monitored for any development of symptoms (WHO, 2020b, Center for Health Protection and HKSAR, 2020). No new cases have been detected in Wuhan since 3 January 2020. However the first case outside China was reported on 13th January 2020 in a Chinese tourist in Thailand with no epidemiological linkage to the Huanan Seafood Wholesale Market. The Chinese Health Authorities have carried out very appropriate and prompt response measures including active case finding, and retrospective investigations of the current cluster of patients which have been completed; The Huanan Seafood Wholesale Market has been temporarily closed to carry out investigation, environmental sanitation and disinfection; Public risk communication activities have been carried out to improve public awareness and adoption of self-protection measures. Technical guidance on novel coronavirus has been developed and will continue to be updated as additional information becomes available. However, many questions about the new coronavirus remain. While it appears to be transmitted to humans via animals, the specific animals and other reservoirs need to be identified, the transmission route, the incubation period and characteristics of the susceptible population and survival rates. At present, there is however very limited clinical information of the 2019-nCoV infection and data are missing in regard to the age range, animal source of the virus, incubation period, epidemic curve, viral kinetics, transmission route, pathogenesis, autopsy findings and any treatment response to antivirals among the severe cases. Once there is any clue to the source of animals being responsible for this outbreak, global public health authorities should examine the trading route and source of movement of animals or products taken from the wild or captive conditions from other parts to Wuhan and consider appropriate trading restrictions or other control measures to limit. The rapid identification and containment of a novel coronavirus virus in a short period of time is a re-assuring and a commendable achievement by China’s public health authorities and reflects the increasing global capacity to detect, identify, define and contain new outbreaks. The latest analysis show that the Wuhan CoV cluster with the SARS CoV.10 (Novel coronavirus - China (01): (HU) WHO, phylogenetic tree Archive Number: 20200112.6885385). This outbreak brings back memories of the novel coronavirus outbreak in China, the severe acute respiratory syndrome (SARS) in China in 2003, caused by a novel SARS-CoV-coronavirus (World Health Organization, 2019a). SARS-CoV rapidly spread from southern China in 2003 and infected more than 3000 people, killing 774 by 2004, and then disappeared – never to be seen again. However, The Middle East Respiratory Syndrome (MERS) Coronavirus (MERS-CoV) (World Health Organization, 2019b), a lethal zoonotic pathogen that was first identified in humans in the Kingdom of Saudi Arabia (KSA) in 2012 continues to emerge and re-emerge through intermittent sporadic cases, community clusters and nosocomial outbreaks. Between 2012 and December 2019, a total of 2465 laboratory-confirmed cases of MERS-CoV infection, including 850 deaths (34.4% mortality) were reported from 27 countries to WHO, the majority of which were reported by KSA (2073 cases, 772 deaths. Whilst several important aspects of MERS-CoV epidemiology, virology, mode of transmission, pathogenesis, diagnosis, clinical features, have been defined, there remain many unanswered questions, including source, transmission and epidemic potential. The Wuhan outbreak is a stark reminder of the continuing threat of zoonotic diseases to global health security. More significant and better targeted investments are required for a more concerted and collaborative global effort, learning from experiences from all geographical regions, through a ‘ONE-HUMAN-ENIVRONMENTAL-ANIMAL-HEALTH’ global consortium to reduce the global threat of zoonotic diseases (Zumla et al., 2016). Sharing experience and learning from all geographical regions and across disciplines will be key to sustaining and further developing the progress being made. Author declarations All authors have a specialist interest in emerging and re-emerging pathogens. FN, RK, OD, GI, TDMc, CD and AZ are members of the Pan-African Network on Emerging and Re-emerging Infections (PANDORA-ID-NET) funded by the European and Developing Countries Clinical Trials Partnership the EU Horizon 2020 Framework Programme for Research and Innovation. AZ is a National Institutes of Health Research senior investigator. All authors declare no conflicts of interest.
Summary Background In December, 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus, emerged in Wuhan, China. Since then, the city of Wuhan has taken unprecedented measures in response to the outbreak, including extended school and workplace closures. We aimed to estimate the effects of physical distancing measures on the progression of the COVID-19 epidemic, hoping to provide some insights for the rest of the world. Methods To examine how changes in population mixing have affected outbreak progression in Wuhan, we used synthetic location-specific contact patterns in Wuhan and adapted these in the presence of school closures, extended workplace closures, and a reduction in mixing in the general community. Using these matrices and the latest estimates of the epidemiological parameters of the Wuhan outbreak, we simulated the ongoing trajectory of an outbreak in Wuhan using an age-structured susceptible-exposed-infected-removed (SEIR) model for several physical distancing measures. We fitted the latest estimates of epidemic parameters from a transmission model to data on local and internationally exported cases from Wuhan in an age-structured epidemic framework and investigated the age distribution of cases. We also simulated lifting of the control measures by allowing people to return to work in a phased-in way and looked at the effects of returning to work at different stages of the underlying outbreak (at the beginning of March or April). Findings Our projections show that physical distancing measures were most effective if the staggered return to work was at the beginning of April; this reduced the median number of infections by more than 92% (IQR 66–97) and 24% (13–90) in mid-2020 and end-2020, respectively. There are benefits to sustaining these measures until April in terms of delaying and reducing the height of the peak, median epidemic size at end-2020, and affording health-care systems more time to expand and respond. However, the modelled effects of physical distancing measures vary by the duration of infectiousness and the role school children have in the epidemic. Interpretation Restrictions on activities in Wuhan, if maintained until April, would probably help to delay the epidemic peak. Our projections suggest that premature and sudden lifting of interventions could lead to an earlier secondary peak, which could be flattened by relaxing the interventions gradually. However, there are limitations to our analysis, including large uncertainties around estimates of R 0 and the duration of infectiousness. Funding Bill & Melinda Gates Foundation, National Institute for Health Research, Wellcome Trust, and Health Data Research UK.
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