COVID’s collateral damage: likelihood of measles resurgence in the United States

Abstract

Background Lockdowns imposed throughout the US to control the COVID-19 pandemic led to a decline in all routine immunizations rates, including the MMR (measles, mumps, rubella) vaccine. It is feared that post-lockdown, these reduced MMR rates will lead to a resurgence of measles. Methods To measure the potential impact of reduced MMR vaccination rates on measles outbreak, this research examines several counterfactual scenarios in pre-COVID-19 and post-COVID-19 era. An agent-based modeling framework is used to simulate the spread of measles on a synthetic yet realistic social network of Virginia. The change in vulnerability of various communities to measles due to reduced MMR rate is analyzed. Results Results show that a decrease in vaccination rate $$(\mathrm{\alpha })$$ ( α ) has a highly non-linear effect on the number of measles cases and this effect grows exponentially beyond a threshold $$(\mathrm{\alpha })$$ ( α ) . At low vaccination rates, faster isolation of cases and higher compliance to home-isolation are not enough to control the outbreak. The overall impact on urban and rural counties is proportional to their population size but the younger children, African Americans and American Indians are disproportionately infected and hence are more vulnerable to the reduction in the vaccination rate. Conclusions At low vaccination rates, broader interventions are needed to control the outbreak. Identifying the cause of the decline in vaccination rates (e.g., low income) can help design targeted interventions which can dampen the disproportional impact on more vulnerable populations and reduce disparities in health. Per capita burden of the potential measles resurgence is equivalent in the rural and the urban communities and hence proportionally equitable public health resources should be allocated to rural regions.

Unintended Consequences of COVID-19 Non-Pharmaceutical Interventions (NPIs) for Population Health and Health Inequalities

“…Although rebounds or outbreaks of infectious diseases after the easing of NPIs were first observed in children and were all caused by non-vaccine-preventable diseases, Oh et al cautioned that future outbreaks of vaccine-preventable diseases (VPDs) could also occur when NPIs are completely lifted [ 118 ]. Thakur et al (2022) [ 124 ] raised the alarm that a decline in routine immunisation rates, including the measles–mumps–rubella (MMR) vaccine, in the US is likely to lead to a resurgence of measles. They recommended that initiatives to identify the cause of the decline in vaccination rates, e.g., low income, can help to design targeted interventions to dampen the disproportionate impact on more vulnerable populations [ 124 ].…”

“…Thakur et al (2022) [ 124 ] raised the alarm that a decline in routine immunisation rates, including the measles–mumps–rubella (MMR) vaccine, in the US is likely to lead to a resurgence of measles. They recommended that initiatives to identify the cause of the decline in vaccination rates, e.g., low income, can help to design targeted interventions to dampen the disproportionate impact on more vulnerable populations [ 124 ].…”

“…Similarly, mental health services might under ordinary circumstances have capacity to deal with the adverse impacts of an emergency, e.g., by providing counselling to those at risk of PTSD, but this capacity was severely curtailed by restrictions imposed early in the pandemic [ 180 , 182 ]. In addition, while childhood immunisation programmes have the potential to offset the influence of NPIs on immunity and susceptibility to infectious diseases, these have been adversely impacted by the pandemic and by reconfiguration of services, giving rise to a risk of re-emergence of diseases such as measles [ 124 ].…”

Are all underimmunized measles clusters equally critical?

“…This model combines various data sets from commercial and public sources, including the US census data, into a common architecture for creating a digital twin of the Virginia population. For more details on the construction of the social network, please see our earlier works [Eubank et al, 2004, Barrett et al, 2009, Thakur et al, 2022, Cadena et al, 2019.…”

“…An infected node will transmit the disease to its susceptible neighbors in the contact network G with a transmission probability β. We use β = 0.5, as estimated from the recent New York City (NYC) outbreak that resulted in 649 cases between September 2018 and August 2019 [Thakur et al, 2022]. The outbreak size was calibrated for Virginia's population size.…”