Optimal control of malaria: combining vector interventions and drug therapies

Impact of population based indoor residual spraying in combination with mass drug administration on malaria incidence and test positivity in a high transmission setting in north eastern Uganda

BACKGROUND

Mass drug administration (MDA) and indoor residual spraying (IRS) are potent malaria burden reduction tools. The impact of combining MDA and IRS is not well documented. We evaluated the impact of MDA + IRS compared to IRS alone at a high transmission site in Eastern Uganda.

METHODS

A quasi-experimental study was implemented in Toroma and Kapujan subcounties in north eastern Uganda. Both subcounties received four rounds of IRS using primiphos-methyl (Acttellic SC300) 6-8 months apart from December 2016 to December 2018. Eligible residents of Kapujan simultaneously received MDA using dihydroartemesinin-piperaquine (DHA-PQ). Health facility data was used to monitor malaria case incidence rate and test positivity rates.

RESULTS

In the MDA + IRS arm, malaria incidence dropped by 83% (IRR: 0·17 (0.16-0.18); p < 0.001) in children under 5 year and by 78% (IRR: 0·22 (0.22-0.23); p < 0.001) in persons aged ≥ 5 years from the pre-intervention to the intervention period. In the IRS arm malaria incidence dropped by 47% (IRR: 0.53 (0.51, 0.56); p < 0.001) in children under 5 years and by 71% 0.29 (0.28, 0.30); p < 0.001) in persons aged ≥ 5 years. A drastic drop occurred immediately after the intervention after which cases slowly increased in both arms. Malaria test positivity rate (TPR) dropped at a rate of 21 (p = 0.003) percentage points per 1000 persons in the MDA + IRS arm compared to the IRS arm. There was a mean decrease of 60 (p-value, 0.040) malaria cases among children under five years and a mean decrease in TPR of 16·16 (p-value, 0.001) in the MDA + IRS arm compared to IRS arm.

INTERPRETATION

MDA significantly reduced malaria burden among children < 5 years however the duration of this impact needs to be further investigated.

A fractional-order mathematical model for malaria and COVID-19 co-infection dynamics

This study proposes a fractional-order mathematical model for malaria and COVID-19 co-infection using the Atangana-Baleanu Derivative. We explain the various stages of the diseases together in humans and mosquitoes, and we also establish the existence and uniqueness of the fractional order co-infection model solution using the fixed point theorem. We conduct the qualitative analysis along with an epidemic indicator, the basic reproduction number R0 of this model. We investigate the global stability at the disease and endemic free equilibrium of the malaria-only, COVID-19-only, and co-infection models. We run different simulations of the fractional-order co-infection model using a two-step Lagrange interpolation polynomial approximate method with the aid of the Maple software package. The results reveal that reducing the risk of malaria and COVID-19 by taking preventive measures will reduce the risk factor for getting COVID-19 after contracting malaria and will also reduce the risk factor for getting malaria after contracting COVID-19 even to the point of extinction.

Effect of ultraviolet LED and trap height on catches of host-seeking anopheline mosquitoes by using a low-cost passive light trap in northeast Brazil

Light traps have been widely used for monitoring malaria vectors, although drawbacks remain. In this context, new tools and attractants are always becoming available to perform monitoring tasks, like the Silva trap, a passive and low-cost LED-light trap for host-seeking anopheline mosquitoes. In this work, the effectiveness of the Silva trap by using UV-LED and at different heights as well as a comparison with the conventional CDC-type (HP) light trap was studied. A total of 9009 mosquitoes and nine species were caught, Anopheles triannulatus, An. argyritarsis, and An. goeldii being the most frequent species. The green (520 nm) and blue (470 nm) LEDs attracted almost equal numbers of anopheline mosquitoes, but UV LEDs (395 nm) attracted a significantly lower number of individuals (Kruskal-Wallis = 19.68, P = 0.0001). Even with the predominance of mosquitoes trapped at the height of 1.5 m, no significant statistical difference was found among the four heights tested (0.5 m; 1.0 m; 1.5 m; 2.0 m). Green-baited Silva traps collected significantly more individuals than incandescent-baited CDC-type traps (U = 60.5; P = 0.0303). LEDs have been useful as light sources for attracting insect vectors and together with a low-cost trap, as the Silva trap, a feasible alternative to conventional trap-based monitoring Anopheles mosquitoes that can be implemented in the field.

Fine-scale estimation of key life-history parameters of malaria vectors: implications for next-generation vector control technologies

Background

Mosquito control has the potential to significantly reduce malaria burden on a region, but to influence public health policy must also show cost-effectiveness. Gaps in our knowledge of mosquito population dynamics mean that mathematical modelling of vector control interventions have typically made simplifying assumptions about key aspects of mosquito ecology. Often, these assumptions can distort the predicted efficacy of vector control, particularly next-generation tools such as gene drive, which are highly sensitive to local mosquito dynamics.

Methods

We developed a discrete-time stochastic mathematical model of mosquito population dynamics to explore the fine-scale behaviour of egg-laying and larval density dependence on parameter estimation. The model was fitted to longitudinal mosquito population count data using particle Markov chain Monte Carlo methods.

Results

By modelling fine-scale behaviour of egg-laying under varying density dependence scenarios we refine our life history parameter estimates, and in particular we see how model assumptions affect population growth rate (R_m), a crucial determinate of vector control efficacy.

Conclusions

Subsequent application of these new parameter estimates to gene drive models show how the understanding and implementation of fine-scale processes, when deriving parameter estimates, may have a profound influence on successful vector control. The consequences of this may be of crucial interest when devising future public health policy.

Graphic abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-021-04789-0.

Optimal vaccination strategy for dengue transmission in Kupang city, Indonesia

Dengue is a public health problem with around 390 million cases annually and is caused by four distinct serotypes. Infection by one of the serotypes provides lifelong immunity to that serotype but have a higher chance of attracting the more dangerous forms of dengue in subsequent infections. Therefore, a perfect strategy against dengue is required. Dengue vaccine with 42-80% efficacy level has been licensed for the use in reducing disease transmission. However, this may increase the likelihood of obtaining the dangerous forms of dengue. In this paper, we have developed single and two-serotype dengue mathematical models to investigate the effects of vaccination on dengue transmission dynamics. The model is validated against dengue data from Kupang city, Indonesia. We investigate the effects of vaccination on seronegative and seropositive individuals and perform a global sensitivity analysis to determine the most influential parameters of the model. A sensitivity analysis suggests that the vaccination rate, the transmission probability and the biting rate have greater effects on the reduction of the proportion of dengue cases. Interestingly, with vaccine implementation, the mosquito-related parameters do not have significant impact on the reduction in the proportion of dengue cases. If the vaccination is implemented on seronegative individuals only, it may increase the likelihood of obtaining the severe dengue. To reduce the proportion of severe dengue cases, it is better to vaccinate seropositive individuals. In the context of Kupang City where the majority of individuals have been infected by at least one dengue serotype, the implementation of vaccination strategy is possible. However, understanding the serotype-specific differences is required to optimise the delivery of the intervention.

Optimal control approaches for combining medicines and mosquito control in tackling dengue

Dengue is a debilitating and devastating viral infection spread by mosquito vectors, and over half the world's population currently live at risk of dengue (and other flavivirus) infections. Here, we use an integrated epidemiological and vector ecology framework to predict optimal approaches for tackling dengue. Our aim is to investigate how vector control and/or vaccination strategies can be best combined and implemented for dengue disease control on small networks, and whether these optimal strategies differ under different circumstances. We show that a combination of vaccination programmes and the release of genetically modified self-limiting mosquitoes (comparable to sterile insect approaches) is always considered the most beneficial strategy for reducing the number of infected individuals, owing to both methods having differing impacts on the underlying disease dynamics. Additionally, depending on the impact of human movement on the disease dynamics, the optimal way to combat the spread of dengue is to focus prevention efforts on large population centres. Using mathematical frameworks, such as optimal control, are essential in developing predictive management and mitigation strategies for dengue disease control.

Optimal control for disease vector management in SIT models: an integrodifference equation approach

Vector-borne diseases are a major public health concern inflicting high levels of disease morbidity and mortality. Vector control is one of the principal methods available to manage infectious disease burden. One approach, releasing modified vectors (such as sterile or GM mosquitoes) Into the wild population has been suggested as an effective method of vector control. However, the effects of dispersal and the spatial distribution of disease vectors (such as mosquitoes) remain poorly studied. Here, we develop a novel mathematical framework using an integrodifference equation (discrete in time and continuous in space) approach to understand the impact of releasing sterile insects into the wild population in a spatially explicit environment. We prove that an optimal release strategy exists and show how it may be characterized by defining a sensitivity variable and an adjoint system. Using simulations, we show that the optimal strategy depends on the spatially varying carrying capacity of the environment.