Wikiversity

WikiJournal Preprints/Pitfalls in Global Response to COVID-19 and its Impact on Global Health

< WikiJournal Preprints

WikiJournal Preprints
Open access • Publication charge free • Public peer review

WikiJournal User Group is a publishing group of open-access, free-to-publish, Wikipedia-integrated academic journals. <seo title=" Wikiversity Journal User Group, WikiJournal Free to publish, Open access, Open-access, Non-profit, online journal, Public peer review "/>

<meta name='citation_doi' value=>

Article information

Abstract

Infectious diseases cause nearly 25 million total annual deaths worldwide. The spectrum of emerging new diseases as well as re-emergence of old diseases is broad, as infectious agents evolve, adapt and spread in response to changing ecosystems, behavior, and moving population patterns. The world is currently experiencing a coronavirus outbreak (initially recognized in Wuhan city of China) that gradually spread in other countries too. COVID-19 has highlighted many gaps in our knowledge, practices, and systems. The critical challenge is how to respond to such catastrophic pandemic and develop rapid and cost-effective methodologies for extensive testing globally, sharing data and developing early warning systems for better preparedness by coordinated efforts. This paper aims to provide a short insight in terms of global preparedness, response, and learning from our shortcoming to improve future approaches dealing with the current outbreak of SARS-CoV-2 and similar future outbreaks.


Introduction edit

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and emerged as the most important viral disease currently. COVID-19 has already affected more than one million people directly and 7.7 billion population of the world indirectly, since its outbreak from presumed zoonotic origin in November-December 2019 from Wuhan, China. The detailed mechanisms for such extensive human-to-human transmission and subsequent clinical outcomes are currently under investigation as one of the most ever investigated studies on any pandemic so far!

SARS-CoV-2 is an enveloped, non-segmented positive-sense RNA, round or oval-shaped virus with a diameter of 60-140 nm belonging to β-coronavirus (β-CoV) capable of infecting mammals.[1] The previously well investigated, highly pathogenic members of this pathogens with human to human transmission are SARS-CoV and Middle East respiratory syndrome coronavirus or MERS-CoV.[2][3][4][5] However, studies have shown that genomic features of MERs-CoV and SARs-CoV are highly diverse from SARS-CoV-2. SARS-CoV-2 might have originated from the bat (suspected natural host of SARS-CoV-2 origin) whereas pangolin (scaly anteater mammal) may have been its intermediate host between bat and human as SARS-CoV-2 appear to be highly similar (96-99%) to coronavirus from bat and pangolin.[6][7][8] Studying genomic polymorphism of SARS-CoV-2 is very important and large sequence data are not only key to its detection and surveillance but will also help in developing epidemiological models of disease spread as well carrying out studies for its detailed functional analysis and thereafter developing vaccines and other interventions.[9][10][11]

The impact of the condition on populations and countries is visible especially in high-income countries with better abilities for testing and more developed health care systems. However, the impact on low- and middle-income countries (LMICs) is yet to be determined as the fallout has not been seen so far. This may be because LMICs usually enjoy less international travelers, have less developed healthcare systems, lack testing capabilities affecting effective screening and case tracing in the community.

It appears that the international community, for several reasons like the social impact or unrest, economic impact, has not responded to this global health emergency as it responded to SARS-CoV-1 about 17 years ago, by rigorous contact tracing and implementing strict case isolation measures.[12] The effectiveness of the approach is evident by the fact that no further cases were reported since 2004.[13] Learning about the global economic impact of SARS-CoV-1, that has been estimated to total $54 billion, according to the World Bank may play a role in response to COVID-19.[14][15] The current COVID-19 infection cycle has already removed trillions of $ out of the global economy. At the same time, $ 6.1 trillion has been invested in mitigation strategies, while the infection seems to have just started to affect some countries. Funding agencies have announced > $15.9 trillion for 1,714 activities to fight against COVID-19. It has now affected all continents except Antarctica and may also reach there shortly.[16]

Global Epidemiology & Gaps in Data edit

The highly polarized world is witnessing the unprecedented outbreak of COVID-19 were high-income countries (HICs) and LMICs are facing the same challenge. The inequities of health status and disease burden caused by pandemic suggest that HICs with the world’s best health care infrastructure are finding it extremely difficult to cope with the consequences of COVID-19. This makes one wonder about how countries where there are wider gaps between having and have not, will cope. Processes of mass testing, active monitoring, and surveillance, extensive data gathering with suitable analysis and interpretation to understand complex communal and global transmission patterns and overall better situational awareness of disease activity are very effective tools of preventing the spread of communicable diseases. Unfortunately, most of these tools are either not available or are unreliable in many LMICs.

Available evidence suggests that patients who reported no pre-existing ("comorbid") medical conditions had a case fatality rate of 0.9%. Pre-existing illnesses that put patients at higher risk of dying from a COVID-19 infection are described in Table 1. The cardiovascular disease appears to be associated with a high fatality rate among all cases as well as confirmed cases as explained in Table 1. The fatality rate related to the different age groups is also different as shown in Table 2 and Table 3.

Table 1. Fatality rate of COVID-19 infected patients who had these co-morbidities

Comorbidities Confirmed cases All cases
Cardiovascular disease 13.2% 10.5%
Diabetes 9.2% 7.3%
Chronic respiratory disease 8.0% 6.3%
Hypertension 8.4% 6.0%
Cancer 7.6% 5.6%
no pre-existing conditions 0.9%

*Death Rate = (number of deaths/number of cases) = probability of dying if infected by the virus (%). This probability differs depending on a pre-existing condition. The percentage shown below does NOT represent in any way the share of deaths by a pre-existing condition. Rather, it represents, for a patient with a given pre-existing condition, the risk of dying if infected by COVID-19.

Table 2. The fatality rate in different age groups across the globe

Age Group Fatality rate
80+ years old 14.8%
70-79 years old 8.0%
60-69 years old 3.6%
50-59 years old 1.3%
40-49 years old 0.4%
30-39 years old 0.2%
20-29 years old 0.2%
10-19 years old 0.2%
0-9 years old no fatalities

*Death Rate = (number of deaths/number of cases) = probability of dying if infected by the virus (%). This probability differs depending on the age group. The percentages represent, for a person in a given age group, the risk of dying if infected with COVID-19.

Table 3. Fatality rate by sex of COVID-19 confirmed cases.

Gender Confirmed cases Overall cases
Male 4.7% 2.8%
Female 2.8% 1.7%

*Death Rate = (number of deaths/number of cases) = probability of dying if infected by the virus (%). This probability differs depending on sex. When reading these numbers, it must be taken into account that smoking in China is much more prevalent among males. Smoking increases the risks of respiratory complications.

High-Income Countries edit

Among all HICs, 13,15776 cases have been reported and the USA has shown the highest number of COVID-19 cases followed by Spain, Italy, Germany, France, and China with a total death toll of 72,711.[17] The outcome of SARS-CoV-2 infection is heavily dependent upon age and comorbidities or underlying health conditions. European countries appear to be among the highest continental risk groups provided how effectively and efficiently they responded by putting in place necessary measures for protection from infection such as travel restrictions, isolations or lockdowns, and massive testing and tracing. The statistics suggest that the CFR due to COVID-19 varies from 21.5% to 0.29% for Guyana and Iceland respectively.[18] However, it is mainly seen in the range of approximately 1% to >10% in the majority of the countries across the globe.[17]

However, calculating CFR or death rates for people with COVID-19 has its own biases within high-income countries, for instance in some countries testing and tracing has been very aggressive such as in South Korea, Singapore, China, and Japan. On the other hand, countries like the U.K. have mainly focused on patients admitted to hospitals with symptoms of COVID-19. This results in reporting CFR error as those who died after diagnosed confirmed COVID-19 cases were recorded as COVID-19 death compared to the ones who died because of SARS-CoV-2 but were not diagnosed.[19] Therefore, death rates being reported on different world meters or country dashboards from different countries may be quite different from the actual number as they are employing different data gathering and reporting policies. It is not necessarily because they are managing the virus any better or that the virus infected fewer or more people. In addition, there are several variables or demographic factors such as vulnerable population, population with underlying comorbidities, number of elderly people in the population, or homeless people. Moreover, taking early initiatives and responses by adopting isolation, social distancing, shutdown, and vigorously applying test and tracing on suspected cases may have helped some countries to recover better by managing the virus better. For instance, countries such as the U.K. have recently taken up the idea of social distancing and are at the stage of adjusting with it while Italian and Spanish are now facing many stringent measures enforced by the respective Governments.

Low and Middle-Income Countries edit

In contrast, recent data have shown that approximately 1.3 billion extremely poor people presently live in 101 countries - predominantly Asian and African countries, where challenges such as poverty, unemployment, malnutrition, lack of appropriate sanitation and hygiene, lack of clean drinking water, diarrheal diseases as well as poor health care system are already impacting the livelihoods of their population.[20][21][22][23][24][25][26] Most of these countries are also considered zoonotic disease hotspots. Looking at the increasing population size of the most populated countries, their current growth rate as well as their population density per square kilometer e.g. 218 people/sq km Nigeria (Annual growth rate 2.7%), 273 people/sq km in Pakistan (Annual growth rate 2.5%), 414 people/sq km India (Annual growth rate 1.8%) and 1169 people/sq km Bangladesh (Annual growth rate 1.7%)[27][28] and urbanization trends may further complicate tackling SARS-CoV-2 challenge.

Interestingly, the statistics and epidemiological data also reflect that countries applying BCG vaccination policy[29] as well as the ones with the malaria endemicity are performing better in terms of negotiating SARS-CoV-2 perhaps of their better residual cross-reacting immunity.[30][29][31] However, unlike most of the HICs, LMICs have been extremely poor in terms of not only testing but whatever testing has been conducted leaves question mark about their validity due to badly performing kits [32] and the other hypothesis can be that sanitation and hygiene may have led to the selection of various etiologies which may have cross protected them against current SARS-CoV-2. It can be also be suggested that the population, in general, are relatively more exposed to novel respiratory infections or any other four mild coronavirus resulting in more immunocompetent against SARS-CoV-2 due to the presence of high titer of cross-reacting antibodies. Certainly, the coming few weeks will be able to help us understand this conundrum better.

The emergence of COVID-19 as an epidemic and then expanding to a pandemic is linked to various factors including ecological modifications, microbiological adaptation, human susceptibility/vulnerability to infection, human demographics and behavior, international trade and travel, poverty and social inequality, poor public health measures, climate change and population size/crowding (number of individual/sq km). It appears that the cross border lockdown of movement implemented by China has helped prevent the spillage from China to its neighboring countries. However, borders have not been very effective at stopping communicable diseases entering the country due to complex international travel patterns due to tourism, trade, coming home on vacations, and extensive religious activities and pilgrimages. Keeping in view the fact that around 1 million people are traveling around the globe every day[33] and seeing the patterns of spread of the virus in particular worst-hit top ten countries, most of them are among the favorite tourist destinations, suggesting lack of timely response to travel associated SARS-CoV-2 spread.

Seasonality & Climate Role in COVID-19 Spread edit

Coronaviruses belong to a family of enveloped viruses and are coated with a lipid bilayer studded with spike-like proteins and this conformation makes its outer layer heat susceptible whereas in cooler conditions its oily coat gets tough to protect virus for a longer period outside the host. This principle is the reason of seasonality of most enveloped respiratory viruses.[34] How long SARS-CoV-2 can persist and whether its transmission will be affected by the seasonal change i.e. transition from winter to summer is a question which the scientific world is anxiously waiting to unfold.[35] The closest cousin, SARS-CoV-1 of 2003 has not left any clues as it was quickly contained in December 2002-July 2003. However, a previous study on coronavirus isolates from respiratory tract infection suggested its winter seasonality (December-April), a pattern normally seen with influenza. The apparent current trends suggest that SARS-CoV-2 has been more successful in cooler climates suggesting its tapering off with the start of warmer months. The rapid transmission trends suggest that SARS-CoV-2 may have preferences for cooler & relative dry conditions although it has shown its presence in a hot and humid environment.[36] On the contrary, there has been a clear indication that the virus spread and external factors such as increasing temperature, wind speed, and relative humidity are linked with the lower incidence of COVID-19.[37][38] However, it is far too early to predict COVID-19’s seasonality by comparing it with known related endemic winter seasonal viruses as we have seen in the case of influenza virus causing about the century-old pandemic of Spanish flu which peaked during the warmer months.[39]

Therefore, a lot will depend on how the season will influence the transmission of SARS-CoV-2 by making it sensitive or not which will eventually determine its fate being a seasonal endemic disease or persisting throughout the year. The testing trends and accurate data gathering, immune-competency, human behavior, traveling or migration patterns, and seasonality together will play a role in determining the overall spread of SARS-CoV-2.[40] It is also possible that if the seasonal impact comes into play like previously reported for the coronavirus family, then the countries where lockdown has been effectively used should be ready for the second wave or surge during the coming winter.

There has been some evidence linking the host immune status to the weather e.g. decreased vitamin D levels during the winter, with increased susceptibility to infections.[41][42] However, this season hypothesis is not supported by another study investigating flu[43] and similarly, another study suggested that cold weather can stimulate the protective innate immune response[44][45] whereas there is stronger evidence that humidity does play role in making humans more vulnerable respiratory to disease. For instance, the dry air, in particular, reduces the amount of mucus coating in airways and lungs which creates the natural protective lining against foreign intruders or viruses.[46]

Preparedness & Response Timeline edit

Chinese authorities reported the first incident of patients suffering pneumonia to WHO on December 31, 2019, from Wuhan City, Hubei Province, and the underlying illness was identified as COVID-19. By January 7, 2020 novel coronavirus (nCOVID-19) was reported and its sequence was shared by January 12, 2020, with the global science community. By the next day, the Chinese had nCov test kits by the subsequent shut-down of Wuhan city and 15 other cities by January 23 and 24 respectively after declaring it a class B notifiable disease earlier on January 20. WHO declared it a public health emergency of global concern by January 30, 2020.[47][48]

It has been more than 3 months since SARS-CoV-2 was first reported and we have witnessed an outburst in several cases after the initial lag phase where the world has been was given a time for preparedness and to respond. However, the global community has not been able to coordinate well to address the challenge and SARS-CoV-2 has successfully traveled beyond the borders (>190 countries). Due to rapid community transmission, most cases are now detected without travel history. The significance of timely testing and tracing to acquire accurate data has been clearly shown from the data that emerged from the village of Vò in northern Italy to better manage the containment of COVID-19. As soon as the first case was reported, the entire village comprising of 3,300 individuals was screened and results indicated that it has already infected 3% of the population mostly without symptoms.[49]

However, even some basic questions are unclear. For example, we do not know how long the pandemic COVID-19 strains remain ‘resident’ in the environment at a particular site and for how long they can be linked to outbreaks in various geographical locations. Linking the microbiology, clinical information, and detailed environmental data with a detailed genomic assessment of SARS-CoV-2 with the ongoing COVID-19 episode and any potential surge afterward will answer these questions. This will also be essential to understand how strains that emanate from human to human contact through respiratory droplets during and after an outbreak change their population structure under various prevailing environmental conditions as well as human behavior. In a timeline starting from December 2019, when the etiology causing pneumonia-like symptoms was not identified and later illness was attributed to SARS-CoV-2,[50] its impact is evident from the increasing case fatality rate (CFR).[17] China’s immediate measures for assessing and reading the situation quickly and reciprocating the response for the sake of public health and security has been exemplary. This is the area that may determine the disease's economic impact and thus, who will be the emerging economic giant(s) globally. It is reflected that how critical the realistic assessment of the COVID-19 situation has been and waging an early response which has certainly been influenced by the human ecosystem and hence varied response time (Fig. 1). Moreover, surveillance information linking with the patient health records will help in better perceiving increased risks and putting in place timely subsequent mitigation approaches including artificial intelligence for well-informed early warning systems.

 
Figure 1: Timeline for risk assessment, planning & decision making to deal with infectious agent of pandemic nature

Industrial and sectoral loses edit

The current COVID-19 pandemic has already affected the global economy both in high and low-income countries. WHO Macro-economic 2030 Agenda for Sustainable Development is under severe shortfall in terms of implementation that will mount inequality, poverty, and commodity dependence especially in developing parts of the world. Banks and lending institutions will be scrambling to roll out stimulus packages. Last week, the IMF estimated that global GDP will shrink to 3% this year compared to its pre-pandemic prediction of a 3.3% expansion and subsequent contraction may continue into 2021. The U.S. accounted for $21.44 trillion that is equivalent to 23% of global GDP in 2019 will contract 40% to 12.9% in the second quarter of this year.[51] Across the Asia-Pacific region, $2.1 trillion of lost output will create a bulk of 23 million jobless workers in 2020. China, being the world’s second-biggest economy with the largest trading community, contributing >28% of global growth during the last five years. Currently, China is suffering a historic slump of 6.8%, which forced to close 460,000 firms with a 29% fall in the registration of new firms in the first quarter of 2020. The current recession in china’s business has already pushed China’s debt to 248.8% of GDP to counter trade war pressures. China’s small and medium enterprises (SMEs) that makeup over 60% of GDP are poised to suffer most. For example, a single industry of rare species rearing valued of $73 billion has been banned by the Chinese Government. Globally the current recession is proving a big setback for leading economies (Table 4).

Table 4. Top 10 economies account for 66% of global GDP.

Serial # Country GDP 2019 Global GDP Share Expected GDP 2020 Potential impact during the Ist quarter of 2020
1 USA $ 21.44 trillion 23.6% $ 22.32 trillion -1.67%
2 China $14.14 trillion 15.5% $15.7 trillion -3.69%
3 Japan $5.2 trillion 5.7% $5.4 trillion -2.23%
4 Germany $4.2 trillion 4.6% $4.5 trillion -1.85%
5 India $2.94 trillion 3.5% $3.3 trillion -2.41%
6 UK $2.83trillion 3.3% $3.2 trillion -1.85%
7 France $2.9 trillion 3.3% $3.1 trillion -1.85%
8 Italy $2.2 trillion 2.5% $2.3 trillion -2.5%
9 Brazil $2.1 trillion 2.1% $2.2 trillion -1.71%
10 Canada $1.8 trillion 1.9% $1.9 trillion -1.57%

The World Bank. "GDP (Current US$)." Accessed June 14, 2020.

Agriculture, forestry, and fishing industry depend on the export of products which is negatively influenced by global demand and supply balance. For instance, many such countries like Australia and Canada as a result of negligible demand by big importer such as China have suffered badly. Similarly, reduced pork farming in China has mounted dependence on Germany with increasing prices and benefits and New Zealand is severely affected due to import bans on Seafood in China. The U.K worker’s shortage has badly impacted the Crop farming industry which is also negatively affected due to reduced prices in the global market. The U.S is expected to experience modest setback because of its relatively low dependence on exports, accounting for approximately 16.0 % of total revenue (https://data.worldbank.org/ ). COVID-19 has significantly impacted the manufacturing, services, natural resources, and tourist trade due to decreased demand for commodities (Table 5).

Table 5. Sectorial Implications of Global pandemic.

Country Manufacturing Services Natural resources Agriculture Tourist trade
China -3.61% -3.67% -1.08% -3.12% -4.64%
USA -2.45% -3.80% -0.21% -3.60% -11.27%
Japan -2.77% -4.62% -2.85% -4.71% -8.35%
Brazil -2.86% -3.14% -1.20% -3.40% -9.28%
Canada -3.25% -3.02% -1.10% -4.30% -9.16%
India -3.98% -4.35% -0.84% -3.36% -8.76%

Envisage simulations www.envisage-project.eu Accessed on June 14, 2020.

Health Care System of High/Low & middle-income countries (HICs & LMICs) edit

The current pandemic has equally challenged the health care system of both HICs and LMICs. Developed countries like the U.S. have an insurance-based, very complicated and expensive health care system that is a mixture public and private partnership. Other countries like Canada and most of Europe have a universal health care system with a basic level of coverage to their citizens. The Healthcare system in China is a combination of both public and private institutions under the insurance umbrella that covers basic health essentials for a wide majority in the country.[52] This pandemic has challenged the existing health care system of developed countries with better basic health indicators, some of which were still unable to cope (Table 6).

Table 6. Basic Indicators of the health care system in HICs & LMICs per 1000

People.

Country HICs/LMICs Physicians Specialist surgical work Force HCWs Nurses Beds
USA HICs 2.6 55 - 14.5 2.9
China HICs 2.0 40 0.8 2.7 4.2
Italy HICs 4.0 142 - 5.7 3.4
Germany HICs 4.2 108 - 13.2 8.3
UK HICs 2.8 133 - 8.2 2.8
France HICs 3.3 59 0.0 11.5 6.5
Japan HICs 2.4 37 - 12.2 13.4
Pakistan LMICs 1.0 6 0.1 0.7 0.6
India LMICs 0.9 7 0.6 1.7 1.2
Uganda LICs 0.2 1 0.2 1.2 0.5

The World Bank date https://data.worldbank.org/ accessed on June 10, 2020.

Pitfalls & Global Science Diplomacy edit

The ongoing fight between rapidly evolving viral infectious agents and subsequent human immune response is not new and we will be encountering frequent invasion of such emerging beyond the border’s pathogens. Therefore, global coordination and preparedness ranging from initial timely, detection capability, coherent surveillance strategies to trace the source of disease etiology and helping each other for fast-track for screening respective populations to avoid the spillage of disease at global level-treating world as one health-one economy as clearly reflected by all current global health & economic indicators.

SARS-CoV-1 helped China to improve its preparedness and launching subsequent timely response including effective nationwide infectious disease surveillance system as well as employing effective regulations to prevent SARS-CoV-2 dissemination. SARS-CoV-2 patients and their close contacts unlike SARS-CoV-1 were immediately isolated. Moreover, the personal protection equipment including the masks which protect, nose, mouth, and eyes were provided to health care workers or frontline responders to better deal with SARS-CoV-2 and the better trained workforce was available to support its public health systems equipped with relevant biomedical technologies. In the last 2 decades, the world has seen three deadly attacks by members of coronavirus family and 2 of them originated from China including ongoing SARS-CoV-2, which helped China to accumulate reasonable experience to deal with the challenge of emerging diseases[53][54][55][56][57][58][59] and similar level of preparedness is now desired by the global community to deal with any future unforeseen situation.

The process of massive testing, active monitoring and surveillance, extensive data gathering with suitable analysis and interpretation to understand complex communal and global transmission patterns, and overall better situational awareness of disease activity and territorial/ global cross border is very effective at stopping communicable diseases. Testing is currently scarce due to global shortages of necessary supplies i.e. swab, extraction, kits, and reagents in addition to accessory other accessory supplies such as masks, personal protective equipment for health caregivers, sanitizers, tissue papers and ventilators for hospitals even for the HICs. The COVID-19 has certainly influenced negatively all these industries by impeding the production and also partly for not reading the situation well to speed up the production line well before it hits the production lines of respective countries.

Therefore, research aimed at rapid, economical, and straightforward diagnostics for infectious diseases is the need of the hour to control the menace better. It is, therefore, imperative for us to have a fresh look at our current health-related research and practices to improve upon our existing diagnostic and hence, health care delivery system. This also underpins the need of developing countries to be self-sufficient in dealing with such health challenges at least at the level of developing rapid massive testing as well as ensuring the availability of accessory essential material such as high-quality masks and sanitizers in particular for their first respondents-health professionals or public health care providers.

Similarly, more awareness about the routes of transmission such as exhaled air in a hospital setting or even in homes where patients are kept in isolation has not been much widely disseminated. The necessary precautions by health caregivers in terms of using personal protective equipment while providing respiratory support therapy and patients while receiving either conventional oxygen therapy or high-flow nasal cannula or non-invasive ventilation should wear medical masks to cut air dispersion.[60] Moreover, adequate properly trained workforce involved in not only educating masses or conducting awareness campaign but also ensuring that quarantine facilities are up to the mark as well as speeding up the process of identifying the cases from evidence gathering, tracing, testing and reporting to break the chain of transmission by nationally designated laboratories as per internationally accepted definitive confirmation.

Therefore, efficient, cost-effective and reliable on-site surveillance or point of use methods will not only help to get timely results for better anticipating the situation but will also facilitate in large scale surveillance of populations as pre-emptive measures where rapid response is desired to counter the global emergency of COVID-19.

Discussion: What We Have Learnt and Way Forward? edit

Nature has its way of teaching lessons as despite several recent indications and reports in the form of SARS-CoV-1 in 2003, MERS in 2012[33][61][62] and recent reports such as Fan et al., 2019 demonstrated the emergence of new virus most likely from coronavirus family, not much-coordinated efforts and measures were put in place to the level where countries can come up with timely effective coordination and cooperation. By looking at the countries of E.U. which are very well connected and with the stronger political resolve to respond to infectious diseases, it appears that COVID-19 indeed exposed their inherent deficiencies although member countries have been giving enough considerations to their public health services as well as have some treaties to collectively respond to public health issues.[63][64]

However, on the contrary in most Asian and African countries where more than 2/3rd of the world population is present, there is no coordination at all and no such treaties exist. The regional and global networks exist in the form of various regional or global cooperation or alliances are ineffective or helpless while facing SARS-CoV-2.

Therefore global health priorities need to be redefined to deal with such calamities in future by sharing real-time information/data, technology transfer, assisting in terms of medical supplies and equipment, promotion of basic and applied research and most of all showing unity and collective response to minimize the socioeconomic burden on LMICs. Global agencies like WHO, the World Organization for Animal Health (OIE), the Food and Agriculture Organization (FAO) of the U.N may take initiative to make the world a safer place to live by catalyzing widespread and effective cooperation among member countries. The importance of correct response by local communities in particular as well as coordinated efforts by the global community, in general, seems to all over the places during this ongoing COVID-19 crisis and it is evident that collaborative efforts at various levels are required to deal this challenge and any other future emerging or re-emerging global infectious diseases challenges (Fig. 2).

 
Figure 2: SARS-CoV-2 initiated Pandemic converging scientific, societal and global efforts to solve international public health emergency

It is loud and clear that time has changed when diagnostic and epidemiological characterizations of infectious diseases were mostly relying on the nature of the illness, time-consuming traditional immuno-surveillance (serology-based methods), culturing, physical characteristics, and metabolic profiles of pathogenic microbes. We have seen the use of remote sensing-drones, mobile technology, and onsite data gathering and massive testing based on diagnostic tests developed from extensive infectious agents genomics data and extensive data gathering for preventing the spread of COVID-19. The importance of basic and applied research in the control of emerging and reemerging diseases was perhaps never realized so badly than now to deal with the challenge of the SARS-CoV-2 era. The use of technologies such as artificial intelligence, robotics, developing epidemiological models, and most of all strengthening the research base of infectious diseases to prepare the workforce to deal with challenges will certainly help in minimizing the future global health risk.

COVID-19 openly has sent the message that it certainly will not be the last pandemic the earth faces, therefore, the preparedness should start from today to deal with the infectious diseases by effective surveillance strategies as it appears that health of one country will have an impact on the other in a chain reaction so Global-One-Health agenda should be pushed harder. We are living in a world where Atomic or Nuclear capabilities will be of much lesser significance than these natural insidious ever-evolving infectious microbes, therefore policy mechanism for better mitigation strategies, surveillance, real-time information sharing, technology transfer, and mutual support somewhat like the International Atomic Energy Agency (IAEA) involving all stakeholder on equal footings would be a way forward. It is, therefore, imperative for us to have a fresh look at our current global coordination on infectious diseases, health-related research, health care delivery system, and practices to improve upon our existing capabilities to deal with any emerging infectious disease of potential pandemic nature.

Additional information edit

Acknowledgements edit

We are deeply indebted to all members of our organization for their selfless help and providing us the best possible environment for this study.

Competing interests edit

All authors have neither any conflict of interest to declare nor have any financial or non-financial interests in the subject matter or materials discussed in this manuscript.

Ethics statement edit

All authors have been personally and actively involved in substantive work leading to the manuscript, and will hold themselves jointly and individually responsible for its content.

References edit

  1. ↑ Zhu, N; Zhang, D; Wang, W; Xingwang, Li MS; Bo, Yang MS (2020). "A novel coronavirus from patients with pneumonia in China, 2019". The New England Journal of Medicine: 382: 727–33. doi:10.1056/nejmoa2001017. https://www.nejm.org/doi/full/10.1056/nejmoa2001017. Retrieved 15 June 2020. 
  2. ↑ Chan, JFW; Yuan, S; Kok, KH; Wang, To KK.; Chu, H (2020). "A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster". Lancet: 395: 514-523. doi:10.1016/S0140-6736(20)30154-9. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30154-9/fulltext. Retrieved 15 June 2020. 
  3. ↑ Su, S; Wong, G; Shi, W; Liu, J; Lai, ACK (2016). "Epidemiology, Genetic Recombination, and Pathogenesis of Coronaviruses". Trends Microbiol: 24: 490-502. doi:10.1016/j.tim.2016.03.003. PMID 27012512. PMC PMC7125511. https://pubmed.ncbi.nlm.nih.gov/27012512/. Retrieved 15 June 2020. 
  4. ↑ Cheng, VC; Lau, SK; Woo, PC; Yuen, KY (2007). "Severe acute respiratory syndrome coronavirus as an agent of emerging and reemerging infection". Clinical Microbiology Reviews: 20: 660-694. doi:10.1128/CMR.00023-07. ISSN 1098-6618. https://cmr.asm.org/content/20/4/660. Retrieved 15 June 2020. 
  5. ↑ Chan, JFW; Lau, SKP; To, KKW; Cheng, VCC; Woo, PCY (2015). "Middle East respiratory syndrome coronavirus: another zoonotic beta-coronavirus causing SARS-like disease". Clinical Microbiology Reviews: 28:465-522. doi:10.1128/CMR.00102-14. PMID 25810418. PMC PMC4402954. https://cmr.asm.org/content/28/2/465. Retrieved 15 June 2020. 
  6. ↑ Zhou, P; Yang, XL; Wang, XG; Hu, B; Zhang, L (2020). "A pneumonia outbreak associated with a new coronavirus of probable bat origin". Nature: 579: 270-273. doi:10.1038/s41586-020-2012-7. https://www.nature.com/articles/s41586-020-2012-7. Retrieved 15 June 2020. 
  7. ↑ Xiao, K; Zhai, J; Feng, Y; Zhou, N; Zhang, X (2020). "Isolation and Characterization of 2019 -nCoV-like Coronavirus from Malayan Pangolins". bioRxiv. doi:10.1101/2020.02.17.951335. https://www.biorxiv.org/content/10.1101/2020.02.17.951335v1. Retrieved 15 June 2020. 
  8. ↑ Lam, TTY; Shum, MHH; Zhu, HC; Tong, GY; Ni, XB (2020). "Identification of 2019-nCoV related coronaviruses in Malayan pangolins in southern China". bioRxiv. doi:10.1101/2020.02.13.945485. https://www.biorxiv.org/content/10.1101/2020.02.13.945485v1. Retrieved 15 June 2020. 
  9. ↑ Liu, Z; Bing, Xing; Zhi, XZ (2020). "The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in china". Chinese journal of Epidemiology 41 (2): 145-151. doi:10.1101/2020.02.13.945485. PMID 32064853. http://rs.yiigle.com/yufabiao/1181998.htm. Retrieved 15 June 2020. 
  10. ↑ Wang, C; Liu, Z; Chen, Z; Huang, X; Xu, M (2020). "The establishment of reference sequence for SARS-CoV-2 and variation analysis". Journal of Medical Virology. doi:10.1002/jmv.25762. https://onlinelibrary.wiley.com/doi/full/10.1002/jmv.25762. Retrieved 15 June 2020. 
  11. ↑ Tang, X; Wu, C; Li, X; Song, Y; Yao, X (2020). "On the origin and continuing evolution of SARS-CoV-2". National Science Review 7 (6): 1012–1023. doi:10.1093/nsr/nwaa036. https://academic.oup.com/nsr/article/7/6/1012/5775463. Retrieved 15 June 2020. 
  12. ↑ Dan, Feng; De Vlas, Sake J.; Fang, Li‐Qun; Han, Xiao‐Na; Zhao, Wen‐Juan (2009). "The SARS epidemic in mainland China: bringing together all epidemiological data". Tropical Medicine and International Health 14 (s1): 4–13. doi:10.1111/j.1365-3156.2008.02145.x. PMID 19508441. PMC PMC7169858. https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-3156.2008.02145.x. Retrieved 15 June 2020. 
  13. ↑ Van, Doremalen N; Morris, DH; Holbrook, MG; Gamble, A; Williamson, BN (2020). "Aerosol and surface stability of HCoV-19 (SARS-CoV-2) compared to SARS-CoV-1". The New England Journal of Medicine. doi:10.1056/NEJMc2004973. https://www.nejm.org/doi/10.1056/NEJMc2004973. Retrieved 15 June 2020. 
  14. ↑ {{cite web |author=World Bank |url=https://openknowledge.worldbank.org/handle/10986/11892 |title=People, Pathogens and Our Planet : The Economics of One Health |year=2012 |publisher=World Bank |location=Washington, DC |access-date=15 June 2020
  15. ↑ Lee, JW; McKibbin, WJ (2004). "Estimating the global economic costs of SARS". In Institute of Medicine (US) Forum on Microbial Threats. Learning from SARS: Preparing for the Next Disease Outbreak: Workshop Summary.. d. Washington (DC): National Academies Press. ISBN 0-309-09154-3. https://www.ncbi.nlm.nih.gov/books/NBK92473/. Retrieved 15 June 2020. 
  16. ↑ Mackenzie, J. S., & Smith, D. W. (2020). "COVID-19: a novel zoonotic disease caused by a coronavirus from China: what we know and what we don't". Microbiology Australia (Advance online publication) 41 (1): 45-50. doi:10.1071/MA20013. https://www.publish.csiro.au/ma/MA20013. Retrieved 15 June 2020. 
  17. ↑ 17.0 17.1 17.2 "Countries where COVID-19 has spread". worldometers.info Acessed on April 30,2020.
  18. ↑ "Global Covid-19 Case Fatality Rates". CEBM Retrieved on june 13,2020.
  19. ↑ "Coronavirus: Country Comparisons are Pointless Unless We Account for These Biases in Testing". Inter Press Service (IPS).
  20. ↑ "The 2019 Global Multidimensional Poverty Index (MPI)". United Nations Development Programme (UNDP). 2019.
  21. ↑ "Malnutrition". United Nations International Children's Emergency Fund (UNICEF). 2020.
  22. ↑ Pennisi, E (2016). "The right gut microbes help infants grow". Science (6275): 802. doi:10.1126/science.351.6275.802. https://science.sciencemag.org/content/351/6275/802. Retrieved 15 June 2020. 
  23. ↑ Gleason, KM; Valeri, L; Shankar, AH; Hasan, MO; Quamruzzaman, Q (2016). "Stunting is associated with blood lead concentration among Bangladeshi children aged 2-3 years". Environmental Health 15: 103. doi:10.1186/s12940-016-0190-4. https://ehjournal.biomedcentral.com/articles/10.1186/s12940-016-0190-4. Retrieved 15 June 2020. 
  24. ↑ Iannotti, LL; Lutter, CK; Waters, WF; Gallegos Riofrio, CAyear=2017. "Eggs early in complementary feeding increase choline pathway biomarkers and DHA: a randomized controlled trial in Ecuador". The American Journal of Clinical Nutrition 106 (6): 1482–1489. doi:10.3945/ajcn.117.160515. https://academic.oup.com/ajcn/article/106/6/1482/4823160. Retrieved 15 June 2020. 
  25. ↑ Panjwani, A; Heidkamp, R (2017). "Complementary Feeding Interventions Have a Small but Significant Impact on Linear and Ponderal Growth of Children in Low- and Middle-Income Countries: A Systematic Review and Meta-Analysis". The Journal of Nutrition 147 (11): 2169S–2178S. doi:10.3945/jn.116.243857. https://academic.oup.com/jn/article/147/11/2169S/4743212. Retrieved 15 June 2020. 
  26. ↑ Miles, MG; Lewis, KD; Kang, G; Parashar, UD; Steele, AD (2012). "A systematic review of rotavirus strain diversity in India, Bangladesh, and Pakistan". Vaccine 30 Suppl 1:A131-9. doi:10.1016/j.vaccine.2011.10.002. PMID 22520122. https://pubmed.ncbi.nlm.nih.gov/22520122/. Retrieved 15 June 2020. 
  27. ↑ "List of countries and dependencies by population density". English Wikipedia.
  28. ↑ "India's Decadal Population Growth 17.64% Compared To Pakistan's 24.78%". India Spend.
  29. ↑ 29.0 29.1 Miller, A.; Reandelar, M. J.; Fasciglione, K.; Roumenova, V.; Li, Y.; Otazu, G. H. (2020). "Correlation between universal BCG vaccination policy and reduced morbidity and mortality for COVID-19: an epidemiological study". medRxiv. doi:10.1101/2020.03.24.20042937. https://www.medrxiv.org/content/10.1101/2020.03.24.20042937v1. Retrieved 15 June 2020. 
  30. ↑ Adams, D.; Alshaban, F. (2020). "COVID-19 cases are less prevalent in countries where malaria is endemic, suggesting a role for anti-malarial drugs as prophylaxis". Eye Reports 6 (1). doi:10.16964/er.v6i1.100. https://www.eyereports.org/index.php/eyereports/article/view/100. Retrieved 15 June 2020. 
  31. ↑ Nioi, M; Napoli, PE (2020). "Global Spread of Coronavirus Disease 2019 and Malaria: An Epidemiological Paradox in the Early Stage of A Pandemic". SSRN. doi:10.2139/ssrn.3554389. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3554389. Retrieved 15 June 2020. 
  32. ↑ "Coronavirus test kits withdrawn in Spain over poor accuracy rate". The Guardian.
  33. ↑ 33.0 33.1 Bokhari, H (2018). "Exploitation of microbial forensics and nanotechnology for the monitoring of emerging pathogens". Critical Reviews in Microbiology: 504-521. doi:10.1080/1040841X.2018.1444013. https://www.tandfonline.com/doi/full/10.1080/1040841X.2018.1444013. Retrieved 15 June 2020. 
  34. ↑ Price, RHM; Graham, C; Ramalingam, S (2019). "Association between viral seasonality and meteorological factors". Scientific Reports 9 (1). doi:10.1038/s41598-018-37481-y. https://www.nature.com/articles/s41598-018-37481-y. Retrieved 15 June 2020. 
  35. ↑ "Coronavirus disease (COVID-19) advice for the public: Mythbusters". WHO.
  36. ↑ Gaunt, ER; Hardie, A; Claas, ECJ; Simmonds, P; Templeton, KE (2010). "Epidemiology and Clinical Presentations of the Four Human Coronaviruses 229E, HKU1, NL63, and OC43 Detected over 3 Years Using a Novel Multiplex Real-Time PCR Method". Journal of Clinical Mirobiology 48 (8): 2940-7. doi:10.1128/JCM.00636-10. https://jcm.asm.org/content/48/8/2940.short. Retrieved 15 June 2020. 
  37. ↑ Chen, B; Liang, H; Xiaomin, Y; Hu, Y; Xu, M (2020). "Roles of meteorological conditions in COVID-19 transmission on a worldwide scale". medRxiv. doi:10.1101/2020.03.16.20037168. https://www.medrxiv.org/content/10.1101/2020.03.16.20037168v1. Retrieved 15 June 2020. 
  38. ↑ Bannister-Tyrrell, M; Meyer, A; Faverjon, C; Cameron, A (2020). "Preliminary evidence that higher temperatures are associated with lower incidence of COVID-19, for cases reported globally up to 29th February 2020". Scientific Reports. doi:10.1101/2020.03.18.20036731. https://www.medrxiv.org/content/10.1101/2020.03.18.20036731v1. Retrieved 15 June 2020. 
  39. ↑ Erkoreka, A (2009). "Origins of the Spanish Influenza pandemic (1918–1920) and its relation to the First World War". Journal of Molecular and Genetic Medicine 3 (2): 190–194. doi:10.4172/1747-0862.1000033. PMID 20076789. PMC PMC2805838. https://pubmed.ncbi.nlm.nih.gov/20076789/. Retrieved 15 June 2020. 
  40. ↑ Lai, S; Bogoch, I; Ruktanonchai, N; Watts, A; Lu, X (2020). "Assessing spread risk of Wuhan novel coronavirus within and beyond China, January-April 2020: a travel network-based modelling study". medRxiv. doi:10.1101/2020.02.04.20020479. https://www.medrxiv.org/content/10.1101/2020.02.04.20020479v2. Retrieved 15 June 2020. 
  41. ↑ Abhimanyu; Coussens, AK (2017). "The role of UV radiation and vitamin D in the seasonality and outcomes of infectious disease". Photochemical & Photobiological Sciences 16 (3): 314-338. doi:10.1039/c6pp00355a. https://pubs.rsc.org/en/content/articlelanding/2017/pp/c6pp00355a#!divAbstract. Retrieved 15 June 2020. 
  42. ↑ Yamshchikov, AV; Desai, NS; Blumberg, HM; Ziegler, TR; Tangpricha, V (2009). "Vitamin D for Treatment and Prevention of Infectious Diseases: A Systematic Review of Randomized Controlled Trials". Endocrine Practice 15 (5): 438-49. doi:10.4158/EP09101.ORR. https://journals.aace.com/doi/abs/10.4158/EP09101.ORR. Retrieved 15 June 2020. 
  43. ↑ Shaman, J; Jeon, CY; Giovannucci, E; Lipsitch, M (2011). "Shortcomings of Vitamin D-Based Model Simulations of Seasonal Influenza". PLoS One 6 (6). doi:10.1371/journal.pone.0020743. e20743. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0020743. Retrieved 15 June 2020. 
  44. ↑ Dowell, SF (2001). "Seasonal variation in host susceptibility and cycles of certain infectious diseases". Emerg Infect Dis 7 (3): 369-74. doi:10.3201/eid0703.010301. PMID 11384511. PMC PMC2631809. https://pubmed.ncbi.nlm.nih.gov/11384511/. Retrieved 15 June 2020. 
  45. ↑ Castellani, JW; M Brenner, IK; Rhind, SG (2002). "Cold exposure: human immune responses and intracellular cytokine expression". Med Sci Sports Exerc 34 (12): 2013-20. doi:10.1097/00005768-200212000-00023. PMID 12471310. https://pubmed.ncbi.nlm.nih.gov/12471310/. Retrieved 15 June 2020. 
  46. ↑ Kudo, E; Song, E; Yockey, LJ; Rakib, T; Wong, PW (2019). "Low ambient humidity impairs barrier function and innate resistance against influenza infection". PNAS 116 (22). doi:10.1073/pnas.1902840116. https://www.pnas.org/content/116/22/10905. Retrieved 15 June 2020. 
  47. ↑ Wu, Z; McGoogan, JM (2019). "Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China". JAMA 323 (13): 1239-1242. doi:10.1001/jama.2020.2648. https://jamanetwork.com/journals/jama/fullarticle/2762130. Retrieved 15 June 2020. 
  48. ↑ Gralinski, LE; Menachery, VD (2020). "Return of the Coronavirus: 2019-nCoV". Viruses 12 (2): 135. doi:10.3390/v12020135. https://www.mdpi.com/1999-4915/12/2/135. Retrieved 15 June 2020. 
  49. ↑ "Coronavirus: Why death and mortality rates differ". BBC Acessed on April 10,2020.
  50. ↑ Cascella, M; Rajnik, M; Cuomo, A; Dulebohn, SC; Di Napoli, R (2020). "Features, Evaluation and Treatment Coronavirus (COVID-19)". StatPearls [Internet]. https://www.ncbi.nlm.nih.gov/books/NBK554776/. Retrieved 15 June 2020. 
  51. ↑ "J.P. Morgan". Encyclopædia Britannica FXStreet report on CNBC News.
  52. ↑ Qingyue, M; Hongwei, Y; Wen, C; Qiang, S; Xiaoyun, L (2015) (PDF). People's Republic of China health system review. World Health Organization. Regional Office for the Western Pacific. ISBN 978 92 9061 728 0. https://apps.who.int/iris/bitstream/handle/10665/208229/9789290617280_eng.pdf?sequence=1&isAllowed=y. Retrieved 15 June 2020. 
  53. ↑ Lu, R; Zhao, X; Li, J; Niu, P; Yang, B (2020). "Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding". The Lancet 395: 556-574. doi:10.1016/S0140-6736(20)30251-8. https://www.thelancet.com/article/S0140-6736(20)30251-8/fulltext. Retrieved 15 June 2020. 
  54. ↑ Chan, JF; Kok, K; Zhu, Z; Chu, H; To, KK (2020). "Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan". Emerging Microbes & Infections 9 (1): 221-236. doi:10.1080/22221751.2020.1719902. https://www.tandfonline.com/doi/full/10.1080/22221751.2020.1719902. Retrieved 15 June 2020. 
  55. ↑ Rossman, H; Keshet, A; Shilo, S; Gavrieli, A; Bauman, T (2020). "A framework for identifying regional outbreak and spread of COVID-19 from one-minute population-wide surveys". medRxiv 48 (8): 2940-7. doi:10.1101/2020.03.19.20038844. https://www.medrxiv.org/content/10.1101/2020.03.19.20038844v2. Retrieved 15 June 2020. 
  56. ↑ Chan, JF; To, KK; Tse, H; Jin, DY; Yuen, KY (2013). "Interspecies transmission and emergence of novel viruses: lessons from bats and birds". Trends Microbiol 21 (10): 544-55. doi:10.1016/j.tim.2013.05.005. PMID 23770275. PMC PMC7126491. https://pubmed.ncbi.nlm.nih.gov/23770275/. Retrieved 15 June 2020. 
  57. ↑ Tian, GZ; Gao, ZC (2014). "Clinical status and its associated early warning per emerging infectious respiratory disease in China". Chinese Medical Journal 127 (17): 3043–3045. PMID 25189942. https://pubmed.ncbi.nlm.nih.gov/25189942/. Retrieved 15 June 2020. 
  58. ↑ Zhong, N; Zeng, G (2006). "What we have learnt from SARS epidemics in China". British Medical Journal 333 (7564): 389–391. doi:10.1136/bmj.333.7564.389. PMID 16916828. PMC PMC1550436. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1550436/. Retrieved 15 June 2020. 
  59. ↑ "Consensus document on the epidemiology of severe acute respiratory syndrome (SARS)" (PDF). WHO. 2003. Archived from the original (PDF) on 16 July 2020.
  60. ↑ Guan, L; Zhou, L; Zhang, J; Peng, W; Chen, R (2020). "More awareness is needed for severe acute respiratory syndrome coronavirus 2019 transmission through exhaled air during non-invasive respiratory support: experience from China". European Respiratory Journal 55: 2000352. doi:10.1183/13993003.00352-2020. https://erj.ersjournals.com/content/55/3/2000352. Retrieved 15 June 2020. 
  61. ↑ Morens, DM; Anthony, Fauci S (2013). "Emerging Infectious Diseases: Threats to Human Health and Global Stability". PLoS Pathog 9 (7). doi:10.1371/journal.ppat.1003467. e1003467. PMID 23853589. PMC PMC3701702. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3701702/. Retrieved 15 June 2020. 
  62. ↑ Van Boheemen, S; de Graaf, M; Lauber, C; Bestebroer, TM; Raj, VS (2012). "Genome characterization of a newly discovered coronavirus associated with acute respiratory distress syndrome in humans". mBio 3 (6). doi:10.1128/mBio.00473-12. e00473-12. PMID 23170002. PMC PMC3509437. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3509437/. Retrieved 15 June 2020. 
  63. ↑ Mossialos, E; Permanand, G; Baeten, R; Hervey, TK (2010) (PDF). Health systems governance in Europe: the role of European Union law and policy (1st ed.). Cambridge University Press. 201010.1017/CBO9780511750496. ISBN 978-0-521-76138-3. https://www.euro.who.int/__data/assets/pdf_file/0006/138147/E94886_prelims.pdf?ua=1. Retrieved 15 June 2020. 
  64. ↑ European Council (2013). "Decision No 1082/2013/EU of the European Parliament and of the Council of 22 October 2013 on serious cross-border threats to health and repealing Decision No 2119/98/EC". Official Journal of the European Union. Retrieved 15 June 2020.
Retrieved from "https://en.wikiversity.org/w/index.php?title=WikiJournal_Preprints/Pitfalls_in_Global_Response_to_COVID-19_and_its_Impact_on_Global_Health&oldid=2247340"