COVID-19 has brought a sense of urgency to decision-making that typically would have taken many months and years of deliberation. Globally, governments are now having to make changes to public policy with far-reaching consequences while in a state of radical uncertainty, without data and evidence to guide them. Central to this uncertainty is the glaring lack of knowledge on just how big the burden of COVID-19 truly is. The pandemic has highlighted concerning gaps in data and weaknesses in surveillance systems that have long hampered public health systems globally, especially in low- and middle- income countries (LMIC).
To fully understand the burden of COVID-19, we urgently need information on the number of active cases, the number of previous cases, the number of deaths from COVID 19, all-cause mortality, and excess deaths. This can help us estimate where we are in the epidemic curve and enable policymakers to target public health measures for maximum impact. Knowledge of the COVID-19 burden also provides a real-time feedback loop for policymakers, helping them to better plan future policies. For example, permitting a nuanced approach to implementing and easing of lockdowns, and other social distancing measures.
In this blog we lay out the challenges related to lack of data facing policymakers in the fight against COVID-19, and call for countries and donors—working alongside agencies such as the Africa CDC and WHO—to urgently invest in stronger data collection and surveillance strategies in LMICs. This can be made possible by building on the strengths of their existing systems as well as ensuring fit-for-purpose systems are developed and operationalised. This will enable a more effective pandemic response and will be a highly valuable legacy after the pandemic.
Why is COVID-19 so difficult to measure?
As the virus that causes COVID-19 began to spread through community transmission, lack of data on the prevalence and incidence became a glaring gap. Reporting on case numbers and deaths are two ways to measure the level of infection and disease burden.
Case reporting depends on the country’s detection mechanism and testing strategy. For example, countries that are doing very few tests per confirmed COVID-19 case are not testing extensively enough to find all cases. Hence, this method would underestimate the burden. Therefore, there is a need for instituting alternative mechanisms to generate supplementary evidence that could complement routine surveillance data; such as, excessive death monitoring and serology surveys.
As our colleagues have pointed out, death reporting is often incomplete and/or unreliable due to systemic weakness in births and deaths registration systems. This means that often the cause of death is recorded inadequately, if at all. Although difficulties in measuring deaths attributable to COVID-19 have also challenged high-income countries, this is particularly the case in certain LMICs. To gauge the true magnitude of the COVID-19 epidemic on population health, we need age-specific all-cause excess mortality data. Africa CDC and WHO have produced valuable guidance on excess death monitoring to look at the overall impact of the pandemic.
Ideally countries would conduct repeated cross-sectional studies across a structured and representative sample of the population so that we understand the situation in terms of new cases and previous cases (via antibody levels). The Coronavirus (COVID-19) Infection Survey is an example of this. However, most countries are not routinely producing these data. Decisions are being made in real time with seemingly little data, because routine surveillance systems, traditional epidemiological cohort studies, civil registration and vital statistics, and data collection systems have been underinvested in during this crisis and for many years before.
For too long the focus of the global community, both in terms of funding and time, has been heavily directed towards modelling, not on data collection. Models, while useful, are only as good as the data available to parametrise them. To model the outcomes of a pandemic like COVID-19, information on the dynamics of human behaviour is as important as reliable epidemiological data, but is currently poorly understood. In LMICs, modelled projections have been less accurate due in part to a lack of local data to verify and adjust the models to the local context. Consequently, a reliance on modelling alone can result in a false sense of confidence when planning policy responses. The kind of models and impact assessments which do not adequately describe uncertainty and identify evidence gaps for more research are especially problematic.
Obstacles to testing
One route to improving the understanding of the burden of COVID-19 is to increase testing. COVID tests fall into two main categories:
- Diagnostic molecular swab tests that detect viral RNA
- Serological tests that detect anti-SARS-CoV-2 immunoglobulins
Diagnostic testing tends to focus on symptomatic patients, but currently in many regions the pandemic is being driven by young people, who may get milder symptoms and never be identified by health care-based testing. Therefore, testing strategies need to go beyond symptomatic testing to identify the magnitude of disease within the community.
Testing regimes have struggled with limited tests, reagents, insufficient lab capacity, and resources in many countries across the world, including some of the richest. In many African countries too, there are long delays in receiving test results, particularly during surges in the number of cases. As cases rise and governments run out resources, ambitious test and trace schemes have been rolled back. Persistent, consistent testing requires both meticulous planning, considerable resources, and a firm commitment from the government. Governments are likely to feel the need to consider trade-offs more acutely due to COVID’s fiscal impact, leading to tight budget constraints.
Serological tests continue to be challenged by suboptimal accuracy, and it is unknown how long immunity lasts. Whilst this limits the value of these tests in individual cases, they remain valuable in sero-surveillance studies to gauge the proportion of the population that have previously been infected, and help to determine the epidemiology of the disease posed by SARS-CoV-2.
How can we find our way again?
COVID-19 demonstrates that now, more than ever, we need to strengthen access to and use of surveillance data to better predict, prevent, and respond to COVID-19 and future health threats. Many countries have already adapted existing systems for COVID-19 surveillance to implement immediate case notification and contract tracing. These provide short-term solutions, but medium-long term capacity building for surveillance also needs to be considered.
Short term measures: Tap into existing systems for surveillance
Active surveillance strategy
A passive surveillance approach will not provide the complete picture. Routine health facility data may provide indirect evidence of COVID-19 transmission, but reporting is frequently delayed, and many patients do not, or cannot, access healthcare services. Given the urgency of the situation and the challenges to rolling out testing, in the short term, there are existing systems that can provide a platform for active collection of necessary data to inform disease burden. Many LMICs have mobilised innovative practices in previous health crises, and these could be built upon and emulated where relevant. For example, Sierra Leonean community health workers were re-deployed to conduct active surveillance during the 2015-16 West African Ebola outbreak, contributing greatly to the faster identification of cases. In Ethiopia–a country that has experienced frequent epidemics of acute-watery diarrhoea–ambitious house-to-house screenings of more than 11 million citizens were conducted by the Ministry of Health, alongside early implementation of airport arrivals screening and quarantining.
Sentinel surveillance strategy
Countries often have sentinel surveillance systems consisting of sites such as hospitals that serve a large section of population. In DRC, the influenza sentinel surveillance system has provided reliable data to estimate the circulation of influenza in the community. Similarly, in response to the Ebola outbreak, many countries enhanced their health security, invested more in developing national public health institutes, and they are drawing on those experiences and resources now.
Participatory surveillance strategy
Simple and effective methods like rapid mobile phone surveys aimed at measuring monthly mortality trends may help. In sub-Saharan Africa, mobile phone penetration has been on a steady increase, making this an attractive proposition to explore.
Medium-long term measures: Build for- purpose systems
The deep-seated weaknesses uncovered by COVID-19 require more than short-term fixes, and there is an urgent need to build fit-for-purpose surveillance systems. There is a role for global actors here. Africa CDC, established as a public health agency to enable member states to share best practice, has leveraged its network during COVID-19 to disseminate resources, guidance and expertise. Through its Partnership to Accelerate COVID-19 Testing (PACT) it has taken ground-breaking steps to increase testing capacity among member states through a continent-owed and supplied procurement platform, bringing the dual benefits of supplying testing materials and equipment at market value to enhance surveillance, and supporting African manufacturers. The initiative was also designed to improve disease surveillance and ensure smart testing through field investigation, active case search, contact tracing, and community engagement. Global health actors should build on the success of these initiatives.
Health security fund
Following the Ebola outbreak in 2014- 2015, many countries demonstrated strong interest in global health security. In the UK, for example, funds were allocated over a five year period to working with selected LMICs to strengthen public health systems in order to better prevent, detect, and respond to future pandemics, in accordance with the WHO International Health Regulations (2005) (IHR). In the most recent recommendations, the IHR Emergency Committee has highlighted the importance of continuing to enhance surveillance.
Any pandemic funds released in response to COVID-19 should include a preparedness component. Peacetime preparedness is key. A global health security challenge fund for pandemic preparedness would help to fill a crucial gap.
A particular focus should be placed on ensuring governments have access to timely national data to enable robust preparedness. Large-scale, cross-sectional, age-stratified, sero-surveillance studies are crucial for monitoring the evolution of the pandemic. Currently, not many African countries have any seroprevalence estimates. Africa CDC and a range of African Union Member States are considering sero-surveillance studies specifically to improve understanding of the status of the epidemic. Public Health England has provided advice and input to Africa CDC's concept note for sero-surveillance across the continent.
Chatham House’s Strengthening National Accountability and Preparedness for Global Health Security (SNAP-GHS) project—funded by PHE—has found that while national public health institutes are often considered focal points for detecting and responding to health emergencies, there are challenges with accessing and using relevant data for preparedness–particularly data that lies outside of the health sector (e.g trade and travel data). A toolkit piloted with Pakistan, Nigeria, and Ethiopia has been developed and could be used to enhance surveillance more widely.
The UK PH Rapid Support Team have launched a platform to share resources and evidence, and discuss best practice to support response to outbreaks in LMICs.
Without knowing just how widespread COVID-19 is, making COVID policies remains a serious challenge. Donors, working in partnership with specialist agencies such as Africa CDC, need to increase their support to countries in this area. Where possible this should be done through building on existing health systems such as networks of community health workers, supplemented with rapid surveys, and investing in long-term improvements in surveillance systems. This will produce long-term benefits to global health security and population health, and will contribute to a much-needed data revolution in global health.
The authors would like to express their thanks to Dr Ebere Okereke for valuable feedback.