Detecting and quantifying pathogens with quick, cost-efficient and sensitive methods is needed across disease systems for addressing pertinent epidemiological questions. Typical methods rely on extracting DNA from collected samples. Here we develop and test an extraction-free method from water bath samples that is both sensitive and efficient for 2 major amphibian pathogens— Batrachochytrium dendrobatidis and B . salamandrivorans . We tested mock samples with known pathogen quantities as well as comparatively assessed detection from skin swabs and water baths from field sampled amphibians. Quantitative PCR (qPCR) directly on lyophilized water baths was able to reliably detect low loads of 10 and 1 zoospores for both pathogens, and detection rates were greater than those of swabs from field samples. Further concentration of samples did not improve detection, and collection container type did not influence pathogen load estimates. This method of lyophilization (i.e. freeze-drying) followed by direct qPCR offers an effective and efficient tool from detecting amphibian pathogens, which is crucial for surveillance efforts and estimating shedding rates for robust epidemiological understanding of transmission dynamics. Furthermore, water bath samples have multiple functions and can be used to evaluate mucosal function against pathogens and characterize mucosal components. The multifunctionality of water bath samples and reduced monetary costs and time expenditures make this method an optimal tool for amphibian disease research and may also prove to be useful in other wildlife disease systems.
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Reframing Optimal Control Problems for Infectious Disease Management in Low-Income Countries
Abstract Optimal control theory can be a useful tool to identify the best strategies for the management of infectious diseases. In most of the applications to disease control with ordinary differential equations, the objective functional to be optimized is formulated in monetary terms as the sum of intervention costs and the cost associated with the burden of disease. We present alternate formulations that express epidemiological outcomes via health metrics and reframe the problem to include features such as budget constraints and epidemiological targets. These alternate formulations are illustrated with a compartmental cholera model. The alternate formulations permit us to better explore the sensitivity of the optimal control solutions to changes in available budget or the desired epidemiological target. We also discuss some limitations of comprehensive cost assessment in epidemiology.
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- NSF-PAR ID:
- 10436208
- Date Published:
- Journal Name:
- Bulletin of Mathematical Biology
- Volume:
- 85
- Issue:
- 4
- ISSN:
- 0092-8240
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Infectious diseases continue to pose a significant threat to the health of humans globally. While the spread of pathogens transcends geographical boundaries, the management of infectious diseases typically occurs within distinct spatial units, determined by geopolitical boundaries. The allocation of management resources within and across regions (the “governance structure”) can affect epidemiological outcomes considerably, and policy-makers are often confronted with a choice between applying control measures uniformly or differentially across regions. Here, we investigate the extent to which uniform and non-uniform governance structures affect the costs of an infectious disease outbreak in two-patch systems using an optimal control framework. A uniform policy implements control measures with the same time varying rate functions across both patches, while these measures are allowed to differ between the patches in a non-uniform policy. We compare results from two systems of differential equations representing transmission of cholera and Ebola, respectively, to understand the interplay between transmission mode, governance structure and the optimal control of outbreaks. In our case studies, the governance structure has a meaningful impact on the allocation of resources and burden of cases, although the difference in total costs is minimal. Understanding how governance structure affects both the optimal control functions and epidemiological outcomes is crucial for the effective management of infectious diseases going forward.
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Abstract The consequences of parasite infection for individual hosts depend on key features of host–parasite ecology underpinning parasite growth and immune defense, such as age, sex, resource supply, and environmental stressors. Scaling these features and their underlying mechanisms from the individual host is challenging but necessary, as they shape parasite transmission at the population level. Translating individual-level mechanisms across scales could inherently improve the way we think about feedbacks among parasitism, the mechanisms driving transmission, and the consequences of human impact and disease control efforts. Here, we use individual-based models (IBMs) based on general metabolic theory, Dynamic Energy Budget (DEB) theory, to scale explicit life-history features of individual hosts, such as growth, reproduction, parasite production, and death, to parasite transmission at the population level over a range of resource supplies focusing on the major human parasite, Schistosoma mansoni, and its intermediate host snail, Biomphalaria glabrata. At the individual level, infected hosts produce fewer parasites at lower resources as competition increases. At the population level, our DEB–IBM predicts brief, but intense parasite peaks early during the host growth season when resources are abundant and infected hosts are few. The timing of these peaks challenges the status quo that high densities of infected hosts produce the highest parasite densities. As expected, high resource supply boosts parasite output, but parasite output also peaks at modest to high host background mortality rates, which parallels overcompensation in stage-structured models. Our combined results reveal the crucial role of individual-level physiology in identifying how environmental conditions, time of the year, and key feedbacks within host–parasite ecology interact to define periods of elevated risk. The testable forecasts from this physiologically-explicit epidemiological model can inform disease management to reduce human risk of schistosome infection.
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Sexually transmitted diseases (STDs) are detrimental to the health and economic well-being of society. Consequently, predicting outbreaks and identifying effective disease interventions through epidemiological tools, such as compartmental models, is of the utmost importance. Unfortunately, the ordinary differential equation compartmental models attributed to the work of Kermack and McKendrick require a duration of infection that follows the exponential or Erlang distribution, despite the biological invalidity of such assumptions. As these assumptions negatively impact the quality of predictions, alternative approaches are required that capture how the variability in the duration of infection affects the trajectory of disease and the evaluation of disease interventions. So, we apply a new family of ordinary differential equation compartmental models based on the quantitymore » « less
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Abstract Strategies to control ongoing biological invasions are often developed by modelling the invasive species' population and aiming to reduce its abundance. However, if the ultimate objective is to protect and restore native species, focussing solely on the invader may not be optimal because it does not account for (i) species interactions that can cause the invader's impacts to depend nonlinearly on its abundance, (ii) collateral damages to native species incurred due to nonspecific removal methods or (iii) native‐invader trait differences.
To identify an invader suppression strategy that maximizes average native population size, we applied optimal control theory to a two‐species model of a native species threatened by an invasive competitor. We examined trade‐offs between iterative physical removals that selectively target invaders and intensifiable chemical control that is nonselective but has higher efficacy.
We found that while iterative removals were capable of supporting large native populations when applied continuously, cost could be prohibitively high. In contrast, when favourable native‐invader trait differences enabled native species to re‐establish more quickly than invaders, intensifiable methods could achieve substantial restoration benefits at lower cost by focussing removal effort into periodic, high‐efficacy events.
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For a case study in Hawaiian streams, we compared how effective two alternative methods of removing invasive live‐bearing fishes (poeciliids) might be at restoring the endemic freshwater goby
Sicyopterus stimpsoni . We found that rotenone (a piscicidal chemical) offered superior benefits when the control budget was small and efficacy was high, but that electrofishing (use of electricity to manually collect target fish) was better with larger budgets and in many lower‐efficacy scenarios.Synthesis and applications. Our findings demonstrate that, by accounting for species interactions and collateral damage, invasive species control strategies can be optimized in light of species traits. Choices about the timing, locations and types of removal events present opportunities to increase the efficiency with which invasive species suppression benefits native species.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s11538-023-01137-4
