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Genomic epidemiology has guided research and policy for various viral pathogens and there has been a parallel effort towards using genomic epidemiology to combat diseases that are caused by eukaryotic pathogens, such as the malaria parasite. However, the central concept of viral genomic epidemiology, namely that of measurably mutating pathogens, does not apply easily to sexually recombining parasites. Here we introduce the related but different concept of measurably recombining malaria parasites to promote convergence around a unifying theoretical framework for malaria genomic epidemiology. Akin to viral phylodynamics, we anticipate that an inferential framework developed around recombination will help guide practical research and thus realize the full public health potential of genomic epidemiology for malaria parasites and other sexually recombining pathogens. The recent pandemic has further highlighted the public health potential of infectious disease genomic epidemiology. For viruses, epidemiological parameters can be estimated under powerful phylodynamic models using both epidemiological and genomic data jointly. An equivalent framework for malaria parasites is lacking because they recombine. Recombination between malaria parasites can generate epidemiologically relevant variation, but recombination is sometimes ineffective, depending dynamically on transmission. This makes it hard to model. It also means it could link epidemiological and genomic processes if they were modeled jointly. Given the potential of recombination, efforts to build a unifying inferential framework around the malaria parasite ancestral recombination graph (ARG) are merited. ARG-based genomic epidemiology could someday be an equivalent of phylodynamics. [ABSTRACT FROM AUTHOR]

National policies designed to control infectious diseases should allocate resources for interventions based on regional estimates of disease burden from surveillance systems. For many infectious diseases, however, there is pronounced seasonal variation in incidence. Policy-makers must routinely manage a public health response to these seasonal fluctuations with limited understanding of their underlying causes. Two complementary and poorly described drivers of seasonal disease incidence are the mobility and aggregation of human populations, which spark outbreaks and sustain transmission, respectively, and may both exhibit distinct seasonal variations. Here we highlight the key challenges that seasonal migration creates when monitoring and controlling infectious diseases. We discuss the potential of new data sources in accounting for seasonal population movements in dynamic risk mapping strategies. [ABSTRACT FROM AUTHOR]

Malaria, HIV, and tuberculosis (TB) collectively account for several million deaths each year, with all three ranking among the top ten killers in low-income countries. Despite being caused by very different organisms, malaria, HIV, and TB present a suite of challenges for mathematical modellers that are particularly pronounced in these infections, but represent general problems in infectious disease modelling, and highlight many of the challenges described throughout this issue. Here, we describe some of the unifying challenges that arise in modelling malaria, HIV, and TB, including variation in dynamics within the host, diversity in the pathogen, and heterogeneity in human contact networks and behaviour. Through the lens of these three pathogens, we provide specific examples of the other challenges in this issue and discuss their implications for informing public health efforts. [ABSTRACT FROM AUTHOR]

Highlights: [•] Mathematical models of malaria control must define human-to-mosquito infectiousness. [•] Uncertainty about human infectiousness can lead to inaccurate policy predictions. [•] More longitudinal data is needed on human infectiousness in endemic regions. [Copyright &y& Elsevier]

Human mobility plays an important role in the dissemination of malaria parasites between regions of variable transmission intensity. Asymptomatic individuals can unknowingly carry parasites to regions where mosquito vectors are available, for example, undermining control programs and contributing to transmission when they travel. Understanding how parasites are imported between regions in this way is therefore an important goal for elimination planning and the control of transmission, and would enable control programs to target the principal sources of malaria. Measuring human mobility has traditionally been difficult to do on a population scale, but the widespread adoption of mobile phones in low-income settings presents a unique opportunity to directly measure human movements that are relevant to the spread of malaria. Here, we discuss the opportunities for measuring human mobility using data from mobile phones, as well as some of the issues associated with combining mobility estimates with malaria infection risk maps to meaningfully estimate routes of parasite importation. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) remains a leading infectious cause of death worldwide. Reducing TB infections and TB-related deaths rests ultimately on stopping forward transmission from infectious to susceptible individuals. Critical to this effort is understanding how human host mobility shapes the transmission and dispersal of new or existing strains of Mycobacterium tuberculosis (Mtb). Important questions remain unanswered. What kinds of mobility, over what temporal and spatial scales, facilitate TB transmission? How do human mobility patterns influence the dispersal of novel Mtb strains, including emergent drug-resistant strains? This review summarizes the current state of knowledge on mobility and TB epidemic dynamics, using examples from three topic areas, including inference of genetic and spatial clustering of infections, delineating source–sink dynamics, and mapping the dispersal of novel TB strains, to examine scientific questions and methodological issues within this topic. We also review new data sources for measuring human mobility, including mobile phone-associated movement data, and discuss important limitations on their use in TB epidemiology. Mobility-related determinants of tuberculosis (TB) transmission and dispersal remain poorly understood, lagging behind recent advances for other epidemic pathogens. Multiple inherent features of TB infection contribute to this knowledge gap, including variable and often prolonged latency of infection, the slow mutation rate of Mycobacterium tuberculosis (Mtb), and high within-host diversity of Mtb populations in many patients. Methods that use geographic distance between infections (for example, those that attempt to detect spatial clustering of infections) may fail to detect meaningful epidemiological patterns if connectivity and transmission linkages between locations are not consistently correlated with distance. Empiric measurement of human mobility patterns may improve the use of these methods in this situation. Geolocated pathogen whole genome sequence data (i.e., sequence data that is linked to the home or clinic location for incident infections), and spatial patterns of genetic diversity and divergence in these data, have yielded important insights into the geographic origins and dispersal of drug-resistant TB and other epidemics. Models of geographic range expansion, which detect genetic signatures of a population expanding away from its origin, may provide an important tool for understanding the origin of novel TB strains. [ABSTRACT FROM AUTHOR]

Epidemiological indicators describing population-level malaria transmission dynamics are widely used to guide policy recommendations. However, the determinants of malaria outcomes within individuals are still poorly understood. This conceptual gap partly reflects the fact that there are few indicators that robustly predict the trajectory of individual infections or clinical outcomes. The parasite multiplication rate (PMR) is a widely used indicator for the Plasmodium intraerythrocytic development cycle (IDC), for example, but its relationship to clinical outcomes is complex. Here, we review its calculation and use in P. falciparum malaria research, as well as the parasite and host factors that impact it. We also provide examples of metrics that can help to link within-host dynamics to malaria clinical outcomes when used alongside the PMR. The PMR is the 'per cycle' fold-change in parasite numbers. It can be derived from parasite counts obtained from in vitro or ex vivo culture, or from patients' peripheral blood by measuring parasitemia and fitting models (the main ones being the exponential and sine-wave approaches). Multiple parasite and host factors can impact the PMR through complex mechanisms involving epigenetics/phenotypic plasticity or resulting from evolution. Disentangling the impact of these different factors on the PMR may be achieved via in vitro experiments and mathematical modeling. Monitoring the parasite biomass and the sequestration index, alongside patient clinical parameters, helps to link within-host dynamics to infection outcomes quantitatively and qualitatively, which PMR alone cannot do accurately across all manifestations of malaria. [ABSTRACT FROM AUTHOR]

Abstract: Genetic disorders of haemoglobin (haemoglobinopathies), including the thalassaemias and sickle cell anaemia, abound in historically malarious regions, due to the protection they provide against death from severe malaria. Despite the overall spatial correlation between malaria and these disorders, inter-population differences exist in the precise combinations of haemoglobinopathies observed. Greece and Italy present a particularly interesting case study: their high frequencies of beta thalassaemia speak to a history of intense malaria selection, yet they possess very little of the strongly malaria protective mutation responsible for sickle cell anaemia, despite historical migrational links with Africa where high frequencies of sickle cell occur. Twentieth century surveys of beta thalassaemia and sickle cell in Greece, Sicily and Sardinia have revealed striking sickle cell ‘hotspots’ – places where the frequency of sickle cell approaches that seen in Africa while neighbouring populations remain relatively sickle cell free. It remains unclear how these hotspots have been maintained over time without sickle cell spreading throughout the region. Here we use a metapopulation model to show that (i) epistasis between the alpha and beta forms of thalassaemia can restrict the spread of sickle cell through a network of linked subpopulations and (ii) the emergence of sickle cell hotspots requires relatively low levels of gene flow, but the aforementioned epistasis increases the chances of hotspots forming. [Copyright &y& Elsevier]

Background: Voluntary individual quarantine and voluntary active monitoring of contacts are core disease control strategies for emerging infectious diseases such as COVID-19. Given the impact of quarantine on resources and individual liberty, it is vital to assess under what conditions individual quarantine can more effectively control COVID-19 than active monitoring. As an epidemic grows, it is also important to consider when these interventions are no longer feasible and broader mitigation measures must be implemented.Methods: To estimate the comparative efficacy of individual quarantine and active monitoring of contacts to control severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we fit a stochastic branching model to reported parameters for the dynamics of the disease. Specifically, we fit a model to the incubation period distribution (mean 5·2 days) and to two estimates of the serial interval distribution: a shorter one with a mean serial interval of 4·8 days and a longer one with a mean of 7·5 days. To assess variable resource settings, we considered two feasibility settings: a high-feasibility setting with 90% of contacts traced, a half-day average delay in tracing and symptom recognition, and 90% effective isolation; and a low-feasibility setting with 50% of contacts traced, a 2-day average delay, and 50% effective isolation.Findings: Model fitting by sequential Monte Carlo resulted in a mean time of infectiousness onset before symptom onset of 0·77 days (95% CI -1·98 to 0·29) for the shorter serial interval, and for the longer serial interval it resulted in a mean time of infectiousness onset after symptom onset of 0·51 days (95% CI -0·77 to 1·50). Individual quarantine in high-feasibility settings, where at least 75% of infected contacts are individually quarantined, contains an outbreak of SARS-CoV-2 with a short serial interval (4·8 days) 84% of the time. However, in settings where the outbreak continues to grow (eg, low-feasibility settings), so too will the burden of the number of contacts traced for active monitoring or quarantine, particularly uninfected contacts (who never develop symptoms). When resources are prioritised for scalable interventions such as physical distancing, we show active monitoring or individual quarantine of high-risk contacts can contribute synergistically to mitigation efforts. Even under the shorter serial interval, if physical distancing reduces the reproductive number to 1·25, active monitoring of 50% of contacts can result in overall outbreak control (ie, effective reproductive number <1).Interpretation: Our model highlights the urgent need for more data on the serial interval and the extent of presymptomatic transmission to make data-driven policy decisions regarding the cost-benefit comparisons of individual quarantine versus active monitoring of contacts. To the extent that these interventions can be implemented, they can help mitigate the spread of SARS-CoV-2.Funding: National Institute of General Medical Sciences, National Institutes of Health. [ABSTRACT FROM AUTHOR]