Why Kenya needs to get everyone behind its Covid-19 response

Though the numbers of Covid-19 cases in Kenya continue to escalate, the country has an opportunity to get ahead of the epidemic curve if policymakers learn from elsewhere, look to their own needs and resources, and act decisively.

After a hesitant start, there have been laudable responses by the Kenyan government, in contrast to other parts of the world.

But in trying to get ahead of the curve, governments absolutely have to take their populations along with them whose consent and compliance is essential. They need to ensure there is transparent and accurate information, with independent scrutiny and oversight, to build the public’s trust and effect greater positive impact together.

Assessments by the World Health Organization (WHO) point to substantial limitations in capacity to respond to the pandemic in Africa. There are shortages of infrastructural capacity such as critical care beds, ventilators and laboratory capacity, as well as human resources for health. In the Kenyan context therefore, the strategy has to be one that emphasises prevention. This rests on two pillars: public health measures and socioeconomic support.

The government has therefore embarked on a strategy to “flatten the curve” but this requires further clarity: what is the estimated scale of the epidemic in the country and hotspot counties, and what is needed in terms of additional health service capacity and socioeconomic support. What, too, of any estimates of the collateral damage caused by the mitigation measures, and the plans for an exit strategy? And is independent multidisciplinary expertise in place to advise all this that considers the Kenyan context?

Recently the government announced a projection of 10,000 Covid cases by end-April based on epidemiological modelling. While it is welcome to have a model informing the strategy, the details — type and content — of this model are not readily available nor are its underlying assumptions. At this time 5,000 cases were expected yet the official tally shows a much smaller number. Is this consistent with the modelling or due to a large caseload in the community with a low reporting rate, or other reasons? The identification of the virus spread, its risk factors, admissions to facilities, co-morbidities, prevention behaviours and disease outcomes across the country will assist in the understanding of the epidemic and inform locally relevant control measures. A regular, timely mandatory reporting dataset for all health services including the private sector is relevant here.

Perhaps the basic reproduction number, which measures the infectiousness of the virus, has reduced over the last few weeks of the current strategy? Surveys can help estimate this and can be done quickly through say, phone or online methods in which individuals are asked to list who they have contacted recently. Such estimates for the population as a whole and its key vulnerable groups like densely populated informal settlements or the large rural population are necessary.

Similarly, it is important to know how well the population is adhering to the mitigation measures in place especially in particular groups such as the transport sector, young people and informal settlements. Here field observational studies by social scientists can help as can mobility data from online, mobile phone data sources and police/transport authorities.

Cultural beliefs and behaviour of the population will contribute greatly to adherence to mitigation measures, risk exposure and spread. So, fact-based public communication and health literacy — the ability of people to obtain and understand health issues — of the population is important. It is lower in Kenya, worsening misinformation and myths which can weaken messaging and mitigation. Whether and how Covid-19 myths and misinformation are changing can be monitored and countered quickly by using behavioural science analysis of, for example, traditional press and social media content. Such scientists can advise on all these aspects and do so from the various Kenyan cultural contexts.

Knowing how much virus is out there, where and in whom means affordable and scalable testing capacity is essential, and will be an important component of epidemic control. Testing strategies need to be identified for community facilities and home settings, possibly using community health workers.

Extensive testing is needed of the symptomatic, even when mild, with tracing and testing of contacts in household, neighbours and others potentially exposed.

This would build on the positive aspects of the Kenyan context that should be maximised: an existing community health workforce that is overseen and implemented at county level and therefore close and accountable to the ground. The government’s planned expansion of health workers should include a large scale-up of the community cadre.

Linked to the health response is the socio-economic one, for keeping the population adherent requires an adequate safety net and protected supply chains. Kenya is an unequal country with high rates of food insecurity, poverty, and infectious diseases such as HIV/Aids and tuberculosis. Is economic necessity pushing people to ignore social distancing despite the economic relief measures announced?

Impact assessments of the costs of the wider societal and economic impact of the virus and its mitigation measures can be done using interviews and surveys to better understand and inform the trade-offs between health gains and socio-economic costs. Already some countries are reporting additional health impacts of the measures with people including children and pregnant women avoiding or presenting late to heath care risking more serious complications, rising cases of domestic violence and mental ill-health.

Kenya’s deadliest days lie ahead. Future prevention options will need credible information and considered judgment or else risk subsequent waves of infection, and preventable additional adverse health and socio-economic impacts. The population is a partner in this.

Dr Mohiddin is a Faculty Member at Aga Khan University’s Department of Population Health. Dr Jasmit Shah and Prof Stanley Luchters, both of Aga Khan University, contributed to this article.