For the 2009 influenza A H1N1 pandemic, in most infected people these epidemiological quantities were short with a day or so to infectiousness and a few days of peak infectiousness to others.3 By ...contrast, for COVID-19, the serial interval is estimated at 4·4–7·5 days, which is more similar to SARS.4 First among the important unknowns about COVID-19 is the case fatality rate (CFR), which requires information on the denominator that defines the number infected. ...the effect of seasons on transmission of COVID-19 is unknown;11 however, with an R0 of 2–3, the warm months of summer in the northern hemisphere might not necessarily reduce transmission below the value of unity as they do for influenza A, which typically has an R0 of around 1·1–1·5.12 Closely linked to these factors and their epidemiological determinants is the impact of different mitigation policies on the course of the COVID-19 epidemic. A key issue for epidemiologists is helping policy makers decide the main objectives of mitigation—eg, minimising morbidity and associated mortality, avoiding an epidemic peak that overwhelms health-care services, keeping the effects on the economy within manageable levels, and flattening the epidemic curve to wait for vaccine development and manufacture on scale and antiviral drug therapies. Avoiding large gatherings of people will reduce the number of super-spreading events; however, if prolonged contact is required for transmission, this measure might only reduce a small proportion of transmissions. ...broader-scale social distancing is likely to be needed, as was put in place in China.
Locally tailored interventions for neglected tropical diseases (NTDs) are becoming increasingly important for ensuring that the World Health Organization (WHO) goals for control and elimination are ...reached. Mathematical models, such as those developed by the NTD Modelling Consortium, are able to offer recommendations on interventions but remain constrained by the data currently available. Data collection for NTDs needs to be strengthened as better data are required to indirectly inform transmission in an area. Addressing specific data needs will improve our modelling recommendations, enabling more accurate tailoring of interventions and assessment of their progress. In this collection, we discuss the data needs for several NTDs, specifically gambiense human African trypanosomiasis, lymphatic filariasis, onchocerciasis, schistosomiasis, soil-transmitted helminths (STH), trachoma, and visceral leishmaniasis. Similarities in the data needs for these NTDs highlight the potential for integration across these diseases and where possible, a wider spectrum of diseases.
Despite the increasing popularity of multi-model comparison studies and their ability to inform policy recommendations, clear guidance on how to conduct multi-model comparisons is not available. ...Herein, we present guidelines to provide a structured approach to comparisons of multiple models of interventions against infectious diseases. The primary target audience for these guidelines are researchers carrying out model comparison studies and policy-makers using model comparison studies to inform policy decisions.
The consensus process used for the development of the guidelines included a systematic review of existing model comparison studies on effectiveness and cost-effectiveness of vaccination, a 2-day meeting and guideline development workshop during which mathematical modellers from different disease areas critically discussed and debated the guideline content and wording, and several rounds of comments on sequential versions of the guidelines by all authors.
The guidelines provide principles for multi-model comparisons, with specific practice statements on what modellers should do for six domains. The guidelines provide explanation and elaboration of the principles and practice statements as well as some examples to illustrate these. The principles are (1) the policy and research question - the model comparison should address a relevant, clearly defined policy question; (2) model identification and selection - the identification and selection of models for inclusion in the model comparison should be transparent and minimise selection bias; (3) harmonisation - standardisation of input data and outputs should be determined by the research question and value of the effort needed for this step; (4) exploring variability - between- and within-model variability and uncertainty should be explored; (5) presenting and pooling results - results should be presented in an appropriate way to support decision-making; and (6) interpretation - results should be interpreted to inform the policy question.
These guidelines should help researchers plan, conduct and report model comparisons of infectious diseases and related interventions in a systematic and structured manner for the purpose of supporting health policy decisions. Adherence to these guidelines will contribute to greater consistency and objectivity in the approach and methods used in multi-model comparisons, and as such improve the quality of modelled evidence for policy.
Abstract
The World Health Organization (WHO) has set elimination as a public health problem (EPHP) as a goal for schistosomiasis. As the WHO treatment guidelines for schistosomiasis are currently ...under revision, we investigate whether school-based or community-wide treatment strategies are required for achieving the EPHP goal. In low- to moderate-transmission settings with good school enrolment, we find that school-based treatment is sufficient for achieving EPHP. However, community-wide treatment is projected to be necessary in certain high-transmission settings as well as settings with low school enrolment. Hence, the optimal treatment strategy depends on setting-specific factors such as the species present, prevalence prior to treatment, and the age profile of infection.
Neglected tropical diseases (NTDs) largely impact marginalised communities living in tropical and subtropical regions. Mass drug administration is the leading intervention method for five NTDs; ...however, it is known that there is lack of access to treatment for some populations and demographic groups. It is also likely that those individuals without access to treatment are excluded from surveillance. It is important to consider the impacts of this on the overall success, and monitoring and evaluation (M&E) of intervention programmes. We use a detailed individual-based model of the infection dynamics of lymphatic filariasis to investigate the impact of excluded, untreated, and therefore unobserved groups on the true versus observed infection dynamics and subsequent intervention success. We simulate surveillance in four groups–the whole population eligible to receive treatment, the whole eligible population with access to treatment, the TAS focus of six- and seven-year-olds, and finally in >20-year-olds. We show that the surveillance group under observation has a significant impact on perceived dynamics. Exclusion to treatment and surveillance negatively impacts the probability of reaching public health goals, though in populations that do reach these goals there are no signals to indicate excluded groups. Increasingly restricted surveillance groups over-estimate the efficacy of MDA. The presence of non-treated groups cannot be inferred when surveillance is only occurring in the group receiving treatment.
It is recognized that changing the current approaches for the control of the neglected tropical diseases will be needed to reach the World Health Organization's (WHO) 2020 goals. Consequently, it is ...important that economic evaluations of the alternative approaches are conducted. A vital component of such evaluations is the issue of how the intervention's costs should be incorporated. We discuss this issue-focusing on mass drug administration. We argue that the common approach of assuming an intervention's cost per treatment is constant, regardless of the number of individuals treated, is a misleading way to consider the delivery costs of mass drug administration due to the occurrence of economies/diseconomies of scale and scope. Greater care and consideration are required when the costs are incorporated into such analyses. Without this, these economic evaluations could potentially lead to incorrect policy recommendations.
In this time of rapidly expanding mass drug administration (MDA) coverage and the new commitments for soil-transmitted helminth (STH) control, it is essential that resources are allocated in an ...efficient manner to have the greatest impact. However, many questions remain regarding how best to deliver STH treatment programmes; these include which age-groups should be targeted and how often. To perform further analyses to investigate what the most cost-effective control strategies are in different settings, accurate cost data for targeting different age groups at different treatment frequencies (in a range of settings) are essential.
Using the electronic databases PubMed, MEDLINE, and ISI Web of Knowledge, we perform a systematic review of costing studies and cost-effectiveness evaluations for potential STH treatment strategies. We use this review to highlight research gaps and outline the key future research needs.
We identified 29 studies reporting costs of STH treatment and 17 studies that investigated its cost-effectiveness. The majority of these pertained to programmes only targeting school-aged children (SAC), with relatively few studies investigating alternative preventive chemotherapy (PCT) treatment strategies. The methods of cost data collection, analysis and reporting were highly variable among the different studies. Only four of the costing studies were found to have high applicability for use in forthcoming economic evaluations. There are also very few studies quantifying the costs of increasing the treatment frequency.
The absence of cost data and inconsistencies in the collection and analysis methods constitutes a major research gap for STH control. Detailed and accurate costs of targeting different age groups or increasing treatment frequency will be essential to formulate cost-effective public health policy. Defining the most cost-effective control strategies in different settings is of high significance during this period of expanding MDA coverage and new resource commitments for STH control.
Since the beginning of the COVID-19 pandemic, the reproduction number
R
has become a popular epidemiological metric used to communicate the state of the epidemic. At its most basic,
R
is defined as ...the average number of secondary infections caused by one primary infected individual.
R
seems convenient, because the epidemic is expanding if
R
>
1
and contracting if
R
<
1
. The magnitude of
R
indicates by how much transmission needs to be reduced to control the epidemic. Using
R
in a naïve way can cause new problems. The reasons for this are threefold: (1) There is not just one definition of
R
but many, and the precise definition of
R
affects both its estimated value and how it should be interpreted. (2) Even with a particular clearly defined
R
, there may be different statistical methods used to estimate its value, and the choice of method will affect the estimate. (3) The availability and type of data used to estimate
R
vary, and it is not always clear what data should be included in the estimation. In this review, we discuss when
R
is useful, when it may be of use but needs to be interpreted with care, and when it may be an inappropriate indicator of the progress of the epidemic. We also argue that careful definition of
R
, and the data and methods used to estimate it, can make
R
a more useful metric for future management of the epidemic.
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