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 Table of Contents  
Year : 2022  |  Volume : 3  |  Issue : 2  |  Page : 115-116

COVID-19: The fourth wave prediction and prevention

Division of Gastrointestinal Sciences, The Wellcome Trust Research Laboratory, Christian Medical College, Vellore, Tamil Nadu, India

Date of Submission01-Jul-2022
Date of Acceptance02-Jul-2022
Date of Web Publication29-Aug-2022

Correspondence Address:
Prof. Gagandeep Kang
Division of Gastrointestinal Sciences, The Wellcome Trust Research Laboratory, Christian Medical College, Vellore - 632 004, Tamil Nadu
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/JME.JME_80_22

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How to cite this article:
Kang G. COVID-19: The fourth wave prediction and prevention. J Med Evid 2022;3:115-6

How to cite this URL:
Kang G. COVID-19: The fourth wave prediction and prevention. J Med Evid [serial online] 2022 [cited 2023 Jun 3];3:115-6. Available from: http://www.journaljme.org/text.asp?2022/3/2/115/354992

How does a wave differ from a seasonal pattern of increase? A reasonable description of a wave might be a sudden and sustained increase that eventually trends downwards. The concept of a wave as opposed to a seasonal pattern requires prior information on how a disease typically behaves and a knowledgebase of what constitutes an expected pattern of infection and illness. For many infections, cholera, rotavirus, influenza, etc., we have sufficient information to have a reasonable ability to predict what is likely to happen by time and place. This does not mean that there will never be increases or outbreaks, but the drivers of those occasional events can mostly be identified.

However, for SARS-CoV2, we do not yet know what 'normal' is going to look like. That makes it difficult to know when a wave is starting. What is a level above which we say a wave has arrived? As an example, does 1000 or 10,000 cases in a single day herald a wave? Or sustained levels above 'x' for a period of a week? Or a rate of change, where every day the rates go up by at least 10% or more? There is no benchmark as yet, but in general, a wave would consist of a combination of numbers, time and pattern of increase.[1]

There are other complications in prediction. If we focus only on national level cases, we know that we miss cases that may be increasing locally. However, in India, there is great variability in the testing strategies and their implementation. As an example, the western states of Maharashtra, Karnataka and Kerala may have reported some of the highest numbers of positive tests consistently over the past couple of years, but serological surveys indicate that levels of infections were similar or lower than in states that reported much lower levels of positive tests.[2]

Further, waves are shaped by our responses. There was much less variability when the pandemic started, because everyone was susceptible. Subsequently, we have had infections vary by geography and variants and have responded with measures to slow spread such as masking and restrictions of movement and by vaccination for prevention.

So what do we need to be able to predict the next wave? And what does prevention mean? First, we need to accept that COVID-19 is not over and achieving elimination is extremely unlikely. There will be further waves of transmission because of the emergence of new variants and waning immunity. Each new successful variant will need to have the ability to evade the immune response generated by past variants and vaccination, so that the virus can multiply in previously infected or vaccinated individual. Although there are several groups around the world that have mapped potential mutations and tried to predict the consequences of those mutations or combinations of mutations[3] on the three key characteristics of the ability to transmit to or infect new hosts, to evade the immune response derived from and maintained by prior infections and vaccination, and the severity of disease caused by the virus variant, there is unpredictability about the timing and location of the next emergent variant.

Once a new variant does emerge, is detected and we learn about its biological behaviour, it then becomes more possible to predict what is likely to happen next. South Africa reported the Omicron variant within 2 weeks of detection and early data showed that this variant had a huge number of mutations and was infecting people who had been previously vaccinated and infected. This information alone was enough to signal the potential of global spread, but how severe the disease would be was initially unknown. Within an additional 2 weeks, it was clear that known vulnerable groups, the elderly those with co-morbidities and the unvaccinated, were at high risk of severe disease. However, in the young, those who were vaccinated and the boosted elderly, the rates of infection were high, but disease was not as severe.[4] In countries where the population had younger median ages, the hospitals and health-care systems were not overwhelmed as they had been during the Delta wave, while countries with older populations saw significant levels of cases, hospitalisations and deaths.

These data are essential to inform infectious disease models. Building and iterating on modelling as new data come in is an essential public health tool. In India, a plethora of models were published, but few had the information needed to best inform mitigation and prevention strategies, because the available data were at a very high level and did not include severity, vaccination history or sequencing.[5]

Not much has changed for data availability in India but based on all that we know today, there are predictions that we can make about the fourth wave in India. A new variant that causes a new wave will necessarily need to be one that can infect people who have been infected before or vaccinated before. This is why maintaining systematic surveillance is important. It is not essential to continue intensive testing when disease is low, but an adaptive calibrated approach can be planned and implemented to ensure that surges are tracked quickly.

The new variant could be anywhere on the spectrum from mild infections to very severe disease, and this is why we need to learn as much about the clinical consequences of infection as early as possible. Global tracking and rapid data sharing are essential to buy time for response, as was done by South Africa. However, the unfortunate restrictions on travel and trade that were imposed on South Africa[6] were completely inappropriate policies based on what we knew by that time.

The tools for prevention are masks and prophylaxis with antivirals and antibodies on exposure, but while masks are available, variant proof and inexpensive, antivirals and antibodies are difficult to deploy, given resource constraints.

Vaccination is and will be the mainstay of protection, particularly for vulnerable populations, and this is why two areas of focus are essential. First, we need to understand the duration and breadth of protection against infection and disease by different variants in different risk groups - and this needs detailed studies of vaccinated populations and schedules with all available vaccines in order to develop the right policies. Second, we need research and development of new vaccines that induce broader and longer lasting protection.[7] so that we have an insurance policy if or when the available first generation vaccines no longer protect us.

In this pandemic, we have scaled up testing, for one disease, to an unprecedented level. The high levels of testing can be ramped down now, but it would be preferable to use the testing capacity for syndromic surveillance, so that we find the next respiratory pandemic early. Investments in testing need to be integrated with data collection with high quality to build strong public health systems that have the knowledge and tools to respond should when there is a fourth or higher order wave. Beyond that, investing in infectious disease modelling and in vaccine and drug research and development are no regrets moves that will serve us well, not just in this pandemic, but all the future ones to come.

  References Top

Zhang SX, Arroyo Marioli F, Gao R, Wang S. A second wave? What do people mean by COVID waves? – A working definition of epidemic waves. Risk Manag Healthc Policy 2021;14:3775-82.  Back to cited text no. 1
Murhekar MV, Bhatnagar T, Thangaraj JW, Saravanakumar V, Santhosh Kumar M, Selvaraju S, et al. Seroprevalence of IgG antibodies against SARS-CoV-2 among the general population and healthcare workers in India, June-July 2021: A population-based cross-sectional study. PLoS Med 2021;18:e1003877.  Back to cited text no. 2
Greaney AJ, Starr TN, Barnes CO, Weisblum Y, Schmidt F, Caskey M, et al. Mapping mutations to the SARS-CoV-2 RBD that escape binding by different classes of antibodies. Nat Commun 2021;12:4196.  Back to cited text no. 3
Abdullah F, Myers J, Basu D, Tintinger G, Ueckermann V, Mathebula M, et al. Decreased severity of disease during the first global omicron variant covid-19 outbreak in a large hospital in Tshwane, South Africa. Int J Infect Dis 2022;116:38-42.  Back to cited text no. 4
Purkayastha S, Bhattacharyya R, Bhaduri R, Kundu R, Gu X, Salvatore M, et al. A comparison of five epidemiological models for transmission of SARS-CoV-2 in India. BMC Infect Dis 2021;21:533.  Back to cited text no. 5
Mendelson M, Venter F, Moshabela M, Gray G, Blumberg L, de Oliveira T, et al. The political theatre of the UK's travel ban on South Africa. Lancet 2021;398:2211-3.  Back to cited text no. 6
Nohynek H, Wilder-Smith A. Does the world still need new covid-19 vaccines? N Engl J Med 2022;386:2140-2.  Back to cited text no. 7


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