Tag Archives: epidemiology

The influence of environmental factors on Covid-19: towards a research agenda

Considerable attention was paid in the early days of the Covid-19 pandemic to its spatial distribution in the hope that environmental factors might be found to play a key role in influencing its spread in two ways: by restricting it to a narrow band of countries with specific environmental factors; and hoping that a rise in temperature in the summer would kill it off.

  • Researchers at Maryland University (Sajadi, M.M. et al., 2020) thus used maps of the early stages of Covid-19 to suggest that it spreads more easily in cold, damp climates, and that its highest incidence would be between latitudes 30-50 N.  At the time, I suggested on 3rd April that there were too many anomalies for this to be valid, that it was only based on limited data (where the coronavirus had spread by early March 2020) and that it was necessary to understand better the actual physical processes involved.  However, the idea that there might be environmental factors that will control Covid-19 still persists.
  • Likewise, in the early days of the pandemic there was much optimism that the new coronavirus might act in similar ways to some of its predecessors and be seasonal in character, waning in the summer months when it gets warmer.  Again, this was in part based on the timing of its outbreak (in China in December 2019 ) and its rapid spread through Europe with an approximately similar timing to seasonal flu.  However, many experts were cautious about this possible scenario (see Jon Cohen in Science, 13th March 2020, and Alvin Powell in the Harvard Gazette, 14th April 2020).

Nevertheless, the much more rapid spread of Covid-19 in Europe and North America than in Africa and South Asia has led some to continue to argue that the devastating impact of lockdown in countries nearer the equator, particularly on the lives of some of the poorest people living there, may be un-necessary if this pattern can indeed be explained by environmental factors.  The lockdown has already been partially rolled back, for example, in countries such as Pakistan (with some factories reopening on 12th April , and congregational prayers at mosques durong Ramadan being permitted from 21st April) and South Africa (with initial steps being taken to reopen the economy on 1st May).  Clearly, the rate and distribution of the spread of Covid-19 is influenced by many factors, including government policies (with the UK performing especially badly, see my recent post),  demographic characteristics (with the elderly being particularly vulnerable), population distribution (spreading slower in sparsely settled areas), characteristics of the several strains and mutations of the Sars-Cov-2 coronavirus (summary in EMCrit), and the inaccuracy and unreliability of reported data about infections and deaths (see my comments here).

The role of environmental factors remains uncertain, despite a considerable amount of research (see systematic review by Mecenas, P. et al., 2020 – thanks to Serge Stinckwich for sharing this) which has sought to draw conclusions from the distribution of cases in parts of the world with different climates, and has suggested that cold and dry conditions helped the spread of the virus whereas warm and wet climates seem to reduce its spread.  A more recent study by Jüni et al. (8th May 2020) has claimed that epidemic growth has little or no association with latitude and temperature, although it has weak negative associations with relative and absolute humidity.  Unfortunately, very few studies have yet sought to do experimental research that actually measures the survivability and ease of spread of Sars-Cov-2 under different real-world environmental conditions.  Moreover, if as appears likely, most infections actually occur indoors, it is not the external climatic conditions that will influence rates of infection but rather the artifical environments created indoors through heating and ventlaltion systems that will be of most significance in influencing its spread.

Two related approaches to this challenge are necessary: identifying its survivabililty in a range of different environments (and surfaces), and analysis of the effect of different environments on the distance that it can be spread by infected people.

Research on the survivability of Sars-Cov-2 in different contexts

Several reported studies have explored the stability of the new coronavirus on different surfaces.  In a widely cited study, van Doremalen et al. (13th  March 2020) suggested that the stability of HCov-19 (Sars-Cov-2) was very similar to that of Sars-Cov-1 (the SARS outbreak in 2003), and that viable virus could be detected as follows:

  • in aerosols up to 3 hours after aerosolization
  • up to 4 hours on copper
  • up to 24 hours on cardboard and up to 47-72 hours on plastic and stainless steel.

This important study has subsequently been used as the standard estimate for the survivability of the coronavirus.  However, it was undertaken in the USA under very specific relatively humidity (for aerosols at 65%; for surfaces at 40%) and temperature conditions (for both at 21-23o C) (See also more recently, van Doremalen et al. 16 April 2020).  A rapid expert review of Sars-Cov-2’s survivability under different conditions (Fineberg, 7th April 2020) notes that the number of experimental studies remains small, but that elevated temperatures seem to reduce its survivability, and that this varies for diffferent materials.  Nevertheless, Fineberg emphasises that laboratory conditions do not necessarily accurately reflect real-world conditions.  In referrring to natural history studies, he also emphasises, as noted above, that conflicting results have emerged because such studies are “hampered by poor quaity data, confounding factors, and insufficient time since the beginning of the pandemix from which to draw conclusions” (p.4).

If a better understanding of Sars-Cov-2’s survivability in different parts of the world is to be gained, it is therefore essential urgently to undertake real world studies of its viability on similar surfaces in various places with different temperature and humidity profiles.

The dispersal distance of Sars-Cov-2

The standard advice across many countries of the world is that people should maintain a minimum distance of 2 m (in some countries 1.5 m) between each other to limit the spread of Covid-19 (see, for example, Public Health England).  This is double the WHO’s advice for the public, which is to “Maintain at least 1 metre (3 feet) distance between yourself and others. Why? When someone coughs, sneezes, or speaks they spray small liquid droplets from their nose or mouth which may contain virus. If you are too close, you can breathe in the droplets, including the COVID-19 virus if the person has the disease“.  The 2 m figure was adopted early by some CDCs, and appears to be more of an approximate early guess (based on the previous Sars-Cov-1 outbreak) that has taken root, rather than an accurate scientifically based figure.

Since then, more rigorous research has been undertaken, much of which suggests that 2 m may not be enough. Setti et al. (23rd April) thus note that Sars-Cov-2 has higher aerosol survivability than did its predecessor, and that a growing body of literature supports a view that “it is plausible that small particles containing the virus may diffuse in indoor environments covering distances up to 10 m from the emission sources”.  They also conclude that “The inter-personal distance of 2 m can be reasonably considered as an effective protection only if everybody wears face masks in daily life activities”. A particularly interesting laboratory based study a month previously by Bourouiba (26th March 2020) provides strong evidence that the turbulent gas clouds formed by sneezes and coughs provide conditions that enable the coronavirus to survive for much longer at greater distances: “The locally moist and warm atmosphere within the turbulent gas cloud allows the contained droplets to evade evaporation for much longer than occurs with isolated droplets“.  She concludes that the “gas cloud and its payload of pathogen-bearing droplets of all sizes can travel 23 to 27 feet (7-8 m)”.  Furthermore, another study by Blocken et al. (9th April) noted that the 1.5 m – 2 m distance was based on people who were standing still, and that there could be a potential aerodynamic effect for people cycling and running.  For someone running at 14.4 km/hr the social distance in the slipstream might be nearer 10 m.

Such studies have been controversial (for a summary, see Eric Niiler in Wired, 14th April), but they highlight that in practice:

  • the “safe’ distance between people is unknown;
  • there is little strong scientific evidence for the 1 m – 2 m recommendations for social distancing; and
  • this distance is highly likely to vary in different environmental contexts.

Not enough conclusive reseach has yet been undertaken on the extent to which environmental factors, such as humidity, pressure, altitude, wind and temperature actually affect how far Sars-Cov-2 will disperse, and at what infectious dose (see Linda Geddes, NewScientist, 27th March 2020, where viral load is also discussed; see also ECDC, 25th March 2020).  It seems likely, though, that dispersal will indeed vary in different conditions, and thus in different parts of the world.  We just don’t yet know how great such variability is.

The latest systematic review published in The Lancet, and cited in The Guardian (2nd June 2020) sugggests that distance does matter, and that not only is 2 m safer than less than 1 m, but also that face masks can indeed reduce substantuially the risk of infection.

Towards a research agenda

This post has emphasised that we actually know remarkably little with certainty about how Sars-Cov-2 physically survives and disperses in different environmental contexts.  This has hugely important ramifications for the spread of Covid-19 in different parts of the world, and thus the mitigating policies and actions that need to be taken.  If, for example, Covid-19 does not survive in hot humid conditions, and is also dispersed over shorter distances in such circumstances, then it might be possible for governments of countries where such conditions prevail not to have to impose such stringent social distancing requirements as those that have been put in place in Europe.

Urgent experimental research is therefore required in real-world environments on:

  • the survivabililty of Sars-Cov-2 in a range of different physical environments (and surfaces), and
  • the effects of different environments on the distance that it can be spread by infected people.

A standard protocol and methodology for such research should be created that could then be used collaboratively by scientists working in different parts of the world to address these crucial issues.  Contrasting environments that would warrant the earliest such research (given the high number of economically poor countries therein) would include: high altitude savanna (as in the Bogotá savanna, and the much lower montane Savanna of the Angolan scarp), tropical and subtropical savanna (as in parts of Brazil and Kenya), tropical rainforests (as in Indonesia and Brazil), semi-arid and arid landscapes (as in much of northern and south-west Africa, the Arabian peninsula, and parts of South Asia).  It is also very important to undertake such resaerch both in urban and rural areas, and indoors as well as outside.  If scientists can indeed co-operate to provide a swift answer to the questions raised in this post, then it would be possible to provide much more tailored advice to governments concerning the mitigating measures (including the use of masks) that they should be taking to protect the highest number of people while also maintaing essential economic activity.

[Updated 8th May, 12th May, 30th May 2020 and 2nd June]

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Filed under Africa, Asia, Covid-19, Geography, India, Pakistan

Crowdsourcing Covid-19 infection rates

Covid-19, 19 March 2020, Source: https://coronavirus.thebaselab.com/

Covid-19, 19 March 2020, Source: https://coronavirus.thebaselab.com/

I have become increasingly frustrated by the continued global reporting of highly misleading figures for the number of Covid-19 infections in different countries.  Such “official” figures are collected in very different ways by governments and can therefore not simply be compared with each other.  Moreover, when they are used to calculate death rates they become much more problematic.  At the very least, everyone who cites such figures should refer to them as “Officially reported Infections”

As I write (19th March 2020, 17.10 UK time), the otherwise excellent thebaselab‘s documentation of the coronavirus’s evolution and spread gives mortality rates (based on deaths as a percentage of infected cases) for China as 4.01%, Italy as 8.34% and the UK as 5.09%.  However, as countries are being overwhelmed by Covid-19, most no longer have the capacity to test all those who fear that they might be infected.  Hence, as the numbers of tests as a percentage of total cases go down, the death rates will appear to go up.  It is fortunately widely suggested that most people who become infected with Covid-19 will only have a mild illness (and they are not being tested in most countries), but the numbers of deaths become staggering if these mortality rates are extrapolated.  Even if only 50% of people are infected (UK estimates are currently between 60% and 80% – see the Imperial College Report of 16th March that estimates that 81% of the UK and US populations will be infected), and such mortality rates are used, the figures (at present rates) become frightening:

  • In Italy, with a total population of 60.48 m, this would mean that 30.24 m people would be infected, which with a mortality rate of 8.34% would imply that 2.52 m people would die;
  • In the UK, with a total population of 66.34 m, this would mean that 33.17 m people would be infected, which with a mortality rate of 5.09% would imply that 1.69 m people would die.

These figures are unrealistic, because only a fraction of the total number of infected people are being tested, and so the reported infection rates are much lower than in reality.  In order to stop such speculations, and to reduce widespread panic, it is essential that all reporting of “Infected Cases” is therefore clarified, or preferably stopped.  Nevertheless, the most likely impact of Covid-19 is still much greater than most people realise or can fully appreciate.  The Imperial College Report (p.16) thus suggests that even if all patients were to be treated, there would still be around 250,000 deaths in Great Britain and 1.1-1.2 m in the USA; doing nothing, means that more than half a million people might die in the UK.

Having accurate data on infection rates is essential for effective policy making and disease management.  Globally, there are simply not enough testing kits or expertise to be able to get even an approximately accurate figure for real infections rates.  Hence, many surrogate measures have been used, all of which have to make complex assumptions about the sample populations from which they are drawn.  An alternative that is fortunately beginning to be considered is the use of digital technologies and social media.  Whilst by no means everyone has access to digital technologies or Internet connectivity, very large samples can be generated.  It is estimated that on average 2.26 billion people use one of the Facebook family of services every day; 30% of the world’s population is a large sample.  Existing crowdsourcing and social media platforms could therefore be used to provide valuable data that might help improve the modelling, and thus the management of this pandemic.

Crowdsourcing

[Great to see that since I first wrote this, Liquid Telecom has used Ushahidi to develop a crowd sourced Covid-19 data gathering initiative]

The violence in Kenya following the disputed Presidential elections in 2007, provided the cradle for the development of the Open Source crowdmapping platform, Ushahidi, which has subsequently been used in responding to disasters such as the earthquakes in Haiti and Nepal, and valuable lessons have been learnt from these experiences.  While there are many challenges in using such technologies, the announcement on 18th March that Ushahidi is waiving its Basic Plan fees for 90 days is very much to be welcomed, and provides an excellent opportunity to use such technologies better to understand (and therefore hopefully help to control) the spread of Covid-19.  However, there is a huge danger that such an opportunity may be missed.

The following (at a bare minimum) would seem to be necessary to maximise the opportunity for such crowdsourcing to be successful:

  • We must act urgently. The failure of countries across the world to act in January, once the likely impact of events in Wuhan unravelled was staggering. If we are to do anything, we have to act now, not least to help protect the poorest countries in the world with the weakest medical services.  Waiting even a fortnight will be too late.
  • Some kind of co-ordination and sharing of good practices is necessary. Whilst a global initiative might be feasible, it would seem more practicable for national initiatives to be created, led and inspired by local activists.  However, for data to be comparable (thereby enabling better modelling to take place) it is crucial for these national initiatives to co-operate and use similar methods and approaches.  There must also be close collaboration with the leading researchers in global infectious disease analysis to identify what the most meaningful indicators might be, as well as international organisations such as the WHO to help disseminate practical findings..
  • An agreed classification. For this to be effective there needs to be a simple agreed classification that people across the world could easily enter into a platform.  Perhaps something along these lines might be appropriate: #CovidS (I think I might have symptoms), #Covid7 (I have had symptoms for 7 days), #Covid14 (I have had symptoms for 14 days), #CovidT (I have been tested and I have it), #Covid0 (I have been tested and I don’t have it), #CovidH (I have been hospitalised), #CovidX (a person has died from it).
  • Practical dissemination.  Were such a platform (or national platforms) to be created, there would need to be widespread publicity, preferably by governments and mobile operators, to encourage as many people as possible to enter their information.  Mutiple languages would need to be incorporated, and the interfaces would have to be as appealing and simple as possible so as to encourage maximum submission of information.

Ushahidi as a platform is particularly appealing, since it enables people to submit information in multiple ways, not only using the internet (such as e-mail and Twitter), but also through SMS messages.  These data can then readily be displayed spatially in real time, so that planners and modellers can see the visual spread of the coronavirus.  There are certainly problems with such an approach, not least concerning how many people would use it and thus how large a sample would be generated, but it is definitely something that we should be exploring collectively further.

Social media

An alternative approach that is hopefully also already being explored by global corporations (but I have not yet read of any such definite projects underway) could be the use of existing social media platforms, such as Facebook/WhatsApp, WeChat or Twitter to collate information about people’s infection with Covid-19. Indeed, I hope that these major corporations have already been exploring innovative and beneficial uses to which their technologies could be put.  However, if this if going to be of any real practical use we must act very quickly.

In essence, all that would be needed would be for there to be an agreed global classification of hashtags (as tentatively suggested above), and then a very widespread marketing programme to encourage everyone who uses these platforms simply to post their status, and any subsequent changes.  The data would need to be released to those undertaking the modelling, and carefully curated information shared with the public.

Whilst such suggestions are not intended to replace existing methods of estimating the spread of infectious diseases, they could provide a valuable additional source of data that could enable modelling to be more accurate.  Not only could this reduce the number of deaths from Covid-19, but it could also help reassure the billions of people who will live through the pandemic.  Of course, such methods also have their sampling challenges, and the data would still need to be carefully interpreted, but this could indeed be a worthwhile initiative that would not be particularly difficult or expensive to initiate if global corporations had the will to do so.

Some final reflections

Already there are numerous new initiatives being set up across the world to find ways through which the latest digital technologies might be used in efforts to minimise the impact of Covid-19. The usual suspects are already there as headlines such as these attest: Blockchain Cures COVID-19 Related Issues in China, AI vs. Coronavirus: How artificial intelligence is now helping in the fight against COVID-19, or Using the Internet of Things To Fight Virus Outbreaks. While some of these may have potential in the future when the next pandemic strikes, it is unlikely that they will have much significant impact  on Covid-19.  If we are going to do anything about it, we must act now with existing well known, easy to use, and reliable digital technologies.

I fear that this will not happen.  I fear that we will see numerous companies and civil society organisations approaching donors with brilliant new innovative “solutions” that will require much funding and will take a year to implement.  By then it will be too late, and they will be forgotten and out of date by the time the next pandemic arrives.  Donors should resist the temptation to fund these.  We need to learn from what happened in West Africa with the spread of Ebola in 2014, when more than 200 digital initiatives seeking to provide information relating to the virus were initiated and funded (see my post On the contribution of ICTs to overcoming the impact of Ebola).  Most (although not all) failed to make any significant impact on the lives and deaths of those affected, and the only people who really benefitted were the companies and the staff working in the civil society organisations who proposed the “innovations”.

This is just a plea for those of us interested in these things to work together collaboratively, collectively and quickly to use what technologies we have at our fingertips to begin to make an impact.  Next week it will probably be too late…

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Filed under Africa, AI, Asia, Empowerment, Health, ICT4D