Gaps in mobility data and implications for modelling epidemic spread: A scoping review and simulation study

Reliable estimates of human mobility are important for understanding the spatial spread of infectious diseases and the effective targeting of control measures. However, when modelling infectious disease dynamics, data on human mobility at an appropriate temporal or spatial resolution are not always available, leading to the common use of model-derived mobility proxies. In this study we reviewed the different data sources and mobility models that have been used to characterise human movement in Africa. We then conducted a simulation study to better understand the implications of using human mobility proxies when predicting the spatial spread and dynamics of infectious diseases. We found major gaps in the availability of empirical measures of human mobility in Africa, leading to mobility proxies being used in place of data. Empirical data on subnational mobility were only available for 17/54 countries, and in most instances, these data characterised long-term movement patterns, which were unsuitable for modelling the spread of pathogens with short generation times (time between infection of a case and their infector). Results from our simulation study demonstrated that using mobility proxies can have a substantial impact on the predicted epidemic dynamics, with complex and non-intuitive biases. In particular, the predicted times and order of epidemic invasion, and the time of epidemic peak in different locations can be underestimated or overestimated, depending on the types of proxies used and the country of interest. Our work underscores the need for regularly updated empirical measures of population movement within and between countries to aid the prevention and control of infectious disease outbreaks. At the same time, there is a need to establish an evidence base to help understand which types of mobility data are most appropriate for describing the spread of emerging infectious diseases in different settings.

Should international borders re-open? The impact of travel restrictions on COVID-19 importation risk

Background

Novel coronavirus disease (COVID-19) has spread across the world at an unprecedented pace, reaching over 200 countries and territories in less than three months. In response, many governments denied entry to travellers arriving from various countries affected by the virus. While several industries continue to experience economic losses due to the imposed interventions, it is unclear whether the different travel restrictions were successful in reducing COVID-19 importations.

Methods

Here we develop a comprehensive probabilistic framework to model daily COVID-19 importations, considering different travel bans. We quantify the temporal effects of the restrictions and elucidate the relationship between incidence rates in other countries, travel flows and the expected number of importations into the country under investigation.

Results

As a cases study, we evaluate the travel bans enforced by the Australian government. We find that international travel bans in Australia lowered COVID-19 importations by 87.68% (83.39 - 91.35) between January and June 2020. The presented framework can further be used to gain insights into how many importations to expect should borders re-open.

Conclusions

While travel bans lowered the number of COVID-19 importations overall, the effectiveness of bans on individual countries varies widely and directly depends on the change in behaviour in returning residents and citizens. Authorities may consider the presented information when planning a phased re-opening of international borders.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12889-021-11616-9).

Limiting mobility during COVID-19, when and to what level? An international comparative study using change point analysis

The year 2020 saw a rapid global spread of the highly contagious novel coronavirus COVID-19. To halt the spread of the disease, decision makers and governments across the world have been forced to limit mobility and human interaction, which led to a complete lockdown and the closure of nonessential businesses and public places in many cities and countries. Although effective in curbing the spread of the disease, such measures have had major social and economic impacts, particularly at locations where a complete lockdown was required. In such unprecedented circumstances, decision makers were faced with the dilemma of deciding on how and when to limit mobility to curb the spread of the disease, while being considerate of the significant economic impacts of enforcing such a lockdown. Limited research in this area meant that decision makers were forced to experiment different courses of action without fully understanding the consequences of those actions. To address this critical gap and to provide decision makers with more insights on how to manage mobility during a global pandemic, this paper conducts statistical change point analysis of mobility data from 10 different countries with the aims of establishing links between mobility trends, COVID-19 infections, and COVID-19 mortality rates across different countries where different policies were adopted. Among other findings, the analysis revealed that slow responders experienced significantly higher mortality rates per 100,000 people and were forced to implement stricter lockdown strategies when compared to early responders. The analysis also shows that operating at 40% level of mobility is achievable if appropriate action is taken early enough. The findings of this study are extremely valuable in helping nations better manage a, highly anticipated, second wave of COVID-19 or any other highly contagious global pandemic.

Modelling airport catchment areas to anticipate the spread of infectious diseases across land and air travel

Air travel is an increasingly important conduit for the worldwide spread of infectious diseases. However, methods to identify which airports an individual may use to initiate travel, or where an individual may travel to upon arrival at an airport is not well studied. This knowledge gap can be addressed by estimating airport catchment areas: the geographic extent from which the airport derives most of its patronage. While airport catchment areas can provide a simple decision-support tool to help delineate the spatial extent of infectious disease spread at a local scale, observed data for airport catchment areas are rarely made publicly available. Therefore, we evaluated a probabilistic choice behavior model, the Huff model, as a potential methodology to estimate airport catchment areas in the United States in data-limited scenarios. We explored the impact of varying input parameters to the Huff model on estimated airport catchment areas: distance decay exponent, distance cut-off, and measures of airport attractiveness. We compared Huff model catchment area patterns for Miami International Airport (MIA) and Harrisburg International Airport (MDT). We specifically compared our model output to observed data sampled for MDT to align model parameters with an established, observed catchment area. Airport catchment areas derived using the Huff model were highly sensitive to changes in model parameters. We observed that a distance decay exponent of 2 and a distance cut-off of 500 km represented the most realistic spatial extent and heterogeneity of the MIA catchment area. When these parameters were applied to MDT, the Huff model produced similar spatial patterns to the observed MDT catchment area. Finally, our evaluation of airport attractiveness showed that travel volume to the specific international destinations of interest for infectious disease importation risks (i.e., Brazil) had little impact on the predicted choice of airport when compared to all international travel. Our work is a proof of concept for use of the Huff model to estimate airport catchment areas as a generalizable decision-support tool in data-limited scenarios. While our work represents an initial examination of the Huff model as a method to approximate airport catchment areas, an essential next step is to conduct a quantitative calibration and validation of the model based on multiple airports, possibly leveraging local human mobility data such as call detail records or online social network data collected from mobile devices. Ultimately, we demonstrate how the Huff model could be potentially helpful to improve the precision of early warning systems that anticipate infectious disease spread, or to incorporate when local public health decision makers need to identify where to mobilize screening infrastructure or containment strategies at a local level.

Essentials of infection prevention in the pediatric population

Application of infection prevention and control (IP&C) measures is synonymous with safety, quality, resource saving, and the rights of all patients, staff and visitors. An extensive literature review was undertaken to identify key IP&C measures that address pediatric-specific requirements, taking into account age-related factors, diseases that occur most frequently among pediatric populations, and the important role of the caregiver in prevention and disease transmission. IP&C initiatives that target and incorporate both human (intrinsic) and environmental (extrinsic) sources of infection are population specific and guide practices and provide safe caring environments when used appropriately.

Categories of care specifically geared to the pediatric age-group (0–14 years) are divided into two categories: within-the-healthcare system and outside-of-the-healthcare setting. By taking into account the child's developmental age; physiological, psycho-social and immunological development; differing impacts on the body's natural defense mechanisms, including procedural, device type and length of utilization; and availability of specific technologies and disciplines, the caregiver IP&C strategies can be developed and tailored to address specific needs. Within-the-healthcare setting strategies focus on surveillance, standard and transmission-based precautions, the immune system, age-related factors, institutional regulations, the family and visitation. Outside the healthcare setting preventative and protective measures are vital for providing maximum and comprehensive care. Care provided within and outside the healthcare setting can either be independent of each other or work together to maximize the health and safety of the child.

This review article highlights the importance of IP&C knowledge, need for strict adherence to approved standards, and need for auditing compliance to achieve the ultimate goal of providing safe, quality care as well as an infection-free environment.