|Year : 2021 | Volume
| Issue : 1 | Page : 4-9
Navigating through COVID-19 Waves: Pandemic Response using Indicator-based Epidemiological Surveillance Tool (PRIEST)
Kiran Kumar Maramraj, Sudeep Naidu, Sougat Ray, Ajit Gopinath, Naveen Chawla
Department of Health Services, O/o Directorate General Medical Services (Navy), Government of India, New Delhi, India
|Date of Submission||06-Jun-2021|
|Date of Decision||08-Jun-2021|
|Date of Acceptance||09-Jun-2021|
|Date of Web Publication||15-Jul-2021|
Surg Cdr (Dr) Kiran Kumar Maramraj
Department of Health Services, O/o Directorate General Medical Services (Navy), Government of India, New Delhi
Source of Support: None, Conflict of Interest: None
|How to cite this article:|
Maramraj KK, Naidu S, Ray S, Gopinath A, Chawla N. Navigating through COVID-19 Waves: Pandemic Response using Indicator-based Epidemiological Surveillance Tool (PRIEST). J Mar Med Soc 2021;23:4-9
|How to cite this URL:|
Maramraj KK, Naidu S, Ray S, Gopinath A, Chawla N. Navigating through COVID-19 Waves: Pandemic Response using Indicator-based Epidemiological Surveillance Tool (PRIEST). J Mar Med Soc [serial online] 2021 [cited 2021 Jul 28];23:4-9. Available from: https://www.marinemedicalsociety.in/text.asp?2021/23/1/4/321598
The world is engrossed in a long-drawn battle against the COVID-19 pandemic. It is a well-known fact that transmission of any infectious disease is the result of a triangular interaction between host, pathogen and the environment. It is a thumb rule that as more individuals in a community become immune against a pathogen, transmission slows down and eventually stops. COVID-19 transmission since December 2019 has been found to follow a “wave-like” (crest–trough) pattern. Wave-like transmission pattern implies that despite the lull periods, future outbreaks and surges of the disease are still possible. Epidemiological alert system with objective indicators is the need of the hour for public health departments to generate early-warning alerts and enable policymakers to take right decisions at the right time.
To meet this objective, we conducted a study in two phases. In the first phase, we examined the history of pandemics occurred in the past globally and also assessed the experiences of various regions and countries. We listed out the possible epidemiological factors that determine the emergence of transmission waves. We then developed a surveillance tool (Pandemic Response using Indicator-based Epidemiological Surveillance Tool [PRIEST]), which can be used to generate early warnings for pandemic surges and recommend the appropriate public health measures.
In the second phase of the study, we applied the PRIEST retrospectively for generating the alerts in Delhi city, which apparently experienced four waves. The data used for the study are open source and available on the website of National Institute of Epidemiology, Indian Council of Medical Research, Government of India. We further retrospectively matched the public health measures implemented by the Delhi government (available on official website of Delhi government) with the timeline of our recommended measures during the last wave.
| Insight of Wave-like Patterns from the Past Pandemics|| |
Historically, the term wave was first used during 1889–1992 Influenza outbreak, also sometimes called Russian Flu (A/H3N8, A/H2N2), which occurred predominantly in Central Asia. It had three distinct waves, which differed in their virulence. The second wave was much more severe, particularly in younger adults. No pandemic has been as deadly as the 1918–1920 Spanish Flu (H1N1) which also occurred in three waves and continued for around 2 years, affecting two-third of the world's population before it subsided. While the first and third waves were fairly mild, the second wave resulted in disastrous global loss. The 2009 Influenza pandemic (H1N1), though mild, had two distinct waves and remains endemic as seasonal influenza even now.
| Country Experiences in the Current COVID-19 Pandemic|| |
The waves of pandemics tend to vary by region/country/state/district. The World Health Organization (WHO) COVID-19 dashboard reveals that the Americas, Europe, Eastern Mediterranean Region, and Western Pacific regions have experienced three waves so far, while South-east Asia and Africa regions experienced two waves. The latter were usually more deadly than the earlier ones. India has experienced two nationwide waves., However, some states and districts have seen more than two waves. For example, Delhi has seen four phases of crests and troughs, signaling four waves. The national capital saw its first peak in June 2020, the second in September 2020, and the third in November 2020, and the fourth in April–May 2021 [Figure 1].,
|Figure 1: Four COVID-19 waves experienced by Delhi, March 20, 2020, to June 13, 2021|
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| Prediction of COVID-19 Waves: The Epidemiological Determinants|| |
The emergence of any further wave or its intensity is multifactorial and includes virus-related, host-related, and vaccine-related factors. One of the main virus-related determinants is emerging variants of concern. The Delta variant, which is considered as more infectious than any other variant in India, has been the primary cause behind the second wave of COVID-19 pandemic, as per a study conducted by the Indian SARS CoV-2 Consortium on Genomics (INSACOG). There are several host-related determinants such as susceptible population subgroups, mass gatherings (political/religious/sports, etc.), international travel, complacency in COVID appropriate behavior (CAB), and re-infection risk due to inadequate duration of immunity of natural infection or vaccine. Herd Immunity (contributed by both natural infections and vaccine) has a major role to play in preventing future waves. The vaccine-related determinants are inadequate coverage, uncertainty of longevity of protection, and low efficacy in preventing the transmission (though the efficacy is considered adequate against reducing severity).
Many areas across the country still have susceptible population subgroups such as rural population and children. However, susceptibility alone would not determine the outcome. For instance, effective reproductive number would be relatively lower in rural areas, being less crowded. Children may acquire infection easily in school settings, but severe form of disease is unlikely as they have relatively under-developed ACE receptors, which SARS CoV-2 needs to impact the lower respiratory system.
Epidemiologically, the probability of having one or more waves exists before the pandemic settles down as an endemic virus in the community. However, the intensity (height) and the speed (steepness) of the wave and its adverse impact on the community (like mortality) may differ in future waves. Further, the country may not experience a nation-wide wave. It may occur at a state/district level with different timeframes, as the current pattern of community immunity is considered as patchy in distribution due to varied natural infection rates and vaccine coverage.
| Pandemic Response using Indicator-based Epidemiological Surveillance Tool [PRIEST]|| |
PRIEST is a simplified, indicator-based, and color-coded three-phase pandemic response tool [Table 1]. These indicators are moving average of daily cases, moving average positivity rate (MAPR), mortality (deaths), and COVID oxygen-bed occupancy (COBO) in healthcare facilities. The first two indicators grouped as Category-I are early-warning indicators and are to be used in combination ("AND” criteria). The latter two indicators grouped as Category-II are severity indicators and need not be used in combination ("OR” criteria).
|Table 1: Epidemiological indicators for generating alerts: COVID-19 Pandemic Response using Indicator-based Epidemiological Surveillance Tool (PRIEST)|
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The thresholds/cutoffs of the indicators used in the tool to declare alert phase (Orange), surge phase (Red), and inter-surge phase (Blue) are shown in [Table 1]. The public health measures recommended for implementation during these phases are given in [Table 2]. Such measures should be geographically limited to where essential, be time-bound and aimed to be as short as reasonably possible.
| Rationale of Indicators in PRIEST|| |
The community-level transmission indicators as recommended by the WHO are case incidence, testing rate, mortality, and hospitalization.,, The type of indicators used for the tool is similar to that of the WHO but modified to facilitate easier evaluation. The tool can be applied on the readily available data from open sources and government websites.
MAPR cutoffs are required to be revised as the laboratory testing modalities, capabilities, and availability increased progressively. The COBO is a reliable indicator for assessment but influenced by changes in hospitalization policies. Use of the indicators in combination is recommended to assess the situation and trends. The evaluation is to be made on local epidemiological data, and it should be as decentralized as possible.
The duration of assessment used to generate early-warning alerts (from Blue to Orange [or] Orange to Red) is usually 7 days but not less than 5 days (corresponds to the median incubation period of 5–6 days). However, the duration used to reverse the alert from Red to Orange is minimum 3 weeks after the declining trend touches 50% of the peak (corresponds to maximum incubation period of 2 weeks and an additional 1 week)., The duration used to withdraw all the alerts (declaring the Blue phase) is 4 weeks after the declining trend touches 50% of the peak (corresponds to double the maximum incubation period).
| Retrospective Generation of COVID-19 Epidemiological Alerts for Delhi Using PRIEST|| |
Epidemiological alerts (Orange, Red, and Blue) were generated retrospectively for Delhi based on the cutoffs of the PRIEST and evaluated. While all three types of alerts could be generated in waves 1 and 4, other two waves were incomplete and found to be a single entity in continuation. The gradient of the fourth wave was so steep that there were only 5 days gap between orange and red alerts. This was possibly due to highly infectious variant (Delta), as the corresponding nation-wide wave was also very steep [Figure 2], [Figure 3], [Figure 4].
|Figure 2: Retrospective application of Indicator-based Epidemiological Surveillance Tool (PRIEST) to “First Wave” of COVID-19 pandemic in Delhi City|
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|Figure 3: Retrospective application of Indicator-based Epidemiological Surveillance Tool (PRIEST) to “Second and Third Waves” of COVID-19 pandemic in Delhi City|
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|Figure 4: Retrospective application of Indicator-based Epidemiological Surveillance Tool (PRIEST) to “Fourth Wave” of COVID-19 pandemic in Delhi City matching with corresponding real alerts generated by Delhi Government|
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While applying the PRIEST to the fourth COVID-19 wave, experienced by the Delhi City, we further compared the timelines of the alerts generated by the tool with that of Delhi government while combating the COVID-19 [Figure 4]. Though the Delhi government had generated the orange alert in time (two days earlier than that indicated by PRIEST), it delayed the generation of red alert by five days. As the rise in cases in the fourth wave was quite rapid compared to previous waves, 5-day delay is considered as very significant. However, Delhi government was very cautious while reverting back to orange alert on June 13, 2021 (against the date of June 5, 2021, suggested by the PRIEST).
| Conclusion|| |
“Fare wind and following seas,” is what every sailor hopes for when he/she is on the high seas. However, nature is bound to surprise, and the sea state may suddenly become rough. A weather forecast before castoff or while at sea may turn out to be true or false, but preparedness and the timely execution of extant SOPs matter the most, when hostile seas have to be dealt with.
The pandemic is global, and therefore, none of the countries are safe until all countries are considered immune. Therefore, preparedness and response actions are required to be continued till the time the pandemic-end is officially declared by the WHO. Surveillance systems should remain robust and a close monitoring of the trends must be continued to generate timely alerts and activate the surge-specific protocols. Many mathematical models predicting the COVID-19 pandemic in India carried a strong element of bias and assumptions. Preparedness (in the form of generation of epidemiological alerts) is thus the key for the way forward and possibly more important than any mathematical predictive modeling. PRIEST assists public health authorities to generate timely and appropriate alerts, thereby guiding policy makers and administrators to intervene with appropriate community measures, striking a balance between lives and livelihood.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Antonovics J. Transmission dynamics: Critical questions and challenges. Philos Trans R Soc Lond B Biol Sci 2017;372:20160087.
Yin Q, Shi T, Dong C, Yan Z. The impact of contact patterns on epidemic dynamics. PLoS One 2017;12:e0173411.
Zhang SX, Marioli FA, Gao R. A second wave? What do people mean by COVID waves? A working definition of epidemic waves. MedRxiv; 2021 [Preprint Review]. doi: 10.1101/2021.02.21.21252147.
National Institute of Epidemiology, Indian Council of Medical Research, Government of India; 2021. Available from: http://covidindiaupdates.in/
. [Last accessed on 2021 Jun 12].
Kempińska-Mirosławska B, Woźniak-Kosek A. The influenza epidemic of 1889-90 in selected European cities--A picture based on the reports of two Poznań daily newspapers from the second half of the nineteenth century. Med Sci Monit 2013;19:1131-41.
Saunders-Hastings PR, Krewski D. Reviewing the history of pandemic influenza: Understanding patterns of emergence and transmission. Pathogens 2016;5:66.
World Health Organization. COVID-19 Dashboard. Available from: https://covid19.who.int/
. [Last accessed on 2021 Jun 12].
Ministry of Health and family Welfare, Government of India. COVID-19 Dashboard; 2021. Available from: https://www.mohfw.gov.in/
. [Last accessed on 2021 Jun 12].
Bhatia R, Abraham P. Lessons learnt during the first 100 days of COVID-19 pandemic in India. Indian J Med Res 2020;151:387-91.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2]