Accounting for regional transmission variability and the impact of malaria control interventions in Ghana: a population level mathematical modelling approach

Many malaria endemic countries, including Ghana, are making tremendous efforts aimed at achieving the 2016–2030 agenda towards malaria control and elimination [1, 2]. In line with this, the Ghana National Malaria Control Programme (NMCP) is guided by a national malaria strategic plan to reduce the burden of malaria by 75.0% across the country by 2020. Among the key strategic interventions adopted by the NMCP for achieving this milestone is the scaling of insecticide-treated bed nets (ITNs)/long-lasting insecticide-treated bed nets (LLINs) distribution, targeted indoor residual spraying (IRS), and improving monitoring activities [3, 4].

In recent years, the NMCP, with support from partners, such as the United States Agency for International Development (USAID), President’s Malaria Initiative (PMI) and The Global Fund to Fight AIDS, Tuberculosis and Malaria, have achieved considerable reductions in malaria-related mortalities, but progress towards substantial reductions in morbidity still remains a challenge [5]. These achievements follow the deployment of new intervention strategies following the adoption of new national policies on the use of the following:

Although these interventions are in place, evaluating their effectiveness using mechanistic models based on locally available data still remains largely unexplored [6]. Despite the contributions of earlier developed mathematical models’ describing the transmission dynamics of malaria in the country, there still exist important knowledge gaps in determining a rational basis for deploying these interventions and evaluating them in the three different ecological zones of Ghana [7].

The dynamics of malaria morbidity generally follow patterns of ecological factors such as rainfall and temperature [8]. There is evidence supporting both this spatial heterogeneity in the ecology of Ghana and the burden of malaria. For this reason, the spatial scale should not be ignored in any malaria investigations of national scale. The country was, therefore, partitioned into zones along three main ecological zones of Ghana, namely the Guinea savannah, transitional forest, and coastal savannah, as described elsewhere [8]. The model was then fitted to data for each zone.

Examples abound of uses of compartmental models for investigation of diseases with the aim of understanding the underlying principles or processes governing dynamics of diseases [9]. Since their introduction into public health by Bernoulli in 1766, applications of mathematical models focused on malaria transmission have continued to attract interest, with several models developed especially in the last fifty years. These models build on those formulated by Ross and vary in complexity and diversity, specifically to elucidate further understanding into the mechanism of malaria transmission in humans [10‐13]. Currently, mathematical models are also being used, among others, to support the formulation of policies aimed at controlling diseases, including monitoring and evaluation of disease incidence [14].

The model developed in this study is based on the basic susceptible-infected-recovered-susceptible (SIRS) model [15, 16], which has been modified to include additional compartments and attributes of the transmission settings in Ghana, such as superinfection. The model structure includes a human population model coupled with a vector model, with climatic elements adapted from Agusto et al. [17].

The objective of this paper is to develop a mathematical model to project the impact of various intervention scenarios of malaria intervention control programmes in Ghana, simulated at a sub-population level that represents the three main ecological zones [7]. The impact of various levels of usage and protective effectiveness, as well as coverage of LLINs and IRS, is also investigated, and prospects of achieving relevant locally and internationally set goals of malaria control and elimination in Ghana are considered.

Ordinary differential equations were used to develop compartmental models for malaria transmission dynamics in the three ecological zones of Ghana. The model diagram for both human and vector populations is as illustrated in Fig. 1. Further details and a description of the models are presented in Additional file 1: S1 Text, the online supplementary files.

The potential impact of malaria interventions was investigated by simulating various implementation scenarios, while taking into account the diversity of morbidity in the three ecological zones across Ghana. These investigations, which were conducted spanning 2018 to 2030, also assessed the prospects of achieving some goals of the Ghana National Malaria Strategic Plan, 2014–2020, as well as those of the World Health Organization (WHO) Global Technical Strategy milestones on malaria control [1].

The models take into account the population sizes of the different transmission settings. Differences in transmission potential for young children, adults, and pregnant women were also considered. The gradual improvement in the data capture and reporting, through the DHIMS infrastructure, at the district level in government health facilities and faith-based private facilities across the country, was accounted for by allowing for various levels of reporting and system improvements from 2008 to 2018. Years of improvement in all suspected cases receiving a malaria diagnostic test was also incorporated (Fig. 3) [32, 35].

The roll out of LLINs on a large scale basis in Ghana began in 2003 [32]. This resulted in a substantial improvement in the proportion of households with at least one LLIN, as well as at least one LLIN per every two members of a household (universal coverage) across the country [24]. For instance, as at 2016, the average proportions of households with at least one LLIN were 89.0%, 74.8%, and 70.0%, compared to 59.0%, 42.5% and 37.6% in 2008 for the Guinea savannah, transitional forest, and coastal savannah zones, respectively [21, 24]. On the other hand, the average coverage levels (universal) of LLINs in 2016 were 65.7%, 50.5% and 49.9% for the Guinea savannah, transitional forest, and coastal savannah zones, respectively [24]. These achievements have largely contributed to the gradual decline in the prevalence of malaria among children aged 6–59 months of age, with the latest (2016) measurement's having fallen to 21.0%, from 27.0% in 2014 [24].

ITN/LLIN usage is relatively low across the country. On average, 56.0%, 45.0%, and 35.2% of the populations in the Guinea savannah, transitional forest and coastal savannah zones, respectively, were reported to have slept in an ITN/LLIN in 2016, a marginal increase from 47.1%, 45.6%, and 32.5% in 2008, for children under the age of five years [21, 24, 36]. These observations follow the results of this study, which suggest that ITN or LLIN usage could be low given the current level of coverage and incidence of malaria across all of the zones. The results from the models show that, with elevated levels of usage of LLINs, which improves PE, a significant number of predicted incidence cases could be averted.

For example, as described earlier, the predicted cases averted by increasing the coverage levels of LLINs to targeted levels of 70.0% and 90.0%, during a three year implementation campaign period, leads to only a marginal improvement from the baseline scenario, without a corresponding increase in the PE of the LLINs (Additional file 2: S2 Fig. 1). This observation may explain why the relatively high universal coverage levels of LLINs currently observed (at least 50.0% across all zones as at 2016) may not be reducing the level of predicted cases as expected.

Even though LLIN deployment has been reported to be one of the most efficient packages, which can lead to a 75.0% reduction in disease, in much of Africa [37], averting more predicted cases through LLINs may only be possible through intensifying the campaign to persuade the population to comply with proper LLIN usage, while continuous efforts are made to sustain the coverage already achieved. Many reasons have been reported for people’s not sleeping in ITN/LLIN including an inability to hang them, real or perceived health concerns, difficulty in breathing when sleeping under them, and other factors [38‐40].

This calls for further and continuous advocacy on the usage of ITNs/LLINs, including the use of formal education channels, and community hang-up/social behaviour communication change campaigns, on the proper usage of the LLINs, while highlighting the potential biting patterns of mosquitoes to avert unnecessary outdoor exposure [18, 19].

Given the proven efficacy of LLINs, and the relatively high coverage levels currently prevailing in the various zones, correspondingly higher reductions in the burden of malaria could have been achieved if the usage of these LLINs was equally as high, as demonstrated throughout the results of various intervention scenarios simulated in this study with increasing levels of usage (Additional file 2: S2 Fig. 1).

Following the WHO guidelines for vector control, Ghana may have attained a high enough LLIN coverage level in selected areas, especially in the Guinea savannah zone, where transmission is highly seasonal and coverage is relatively higher, to begin the roll out of IRS on a targeted large-scale basis as a complementary vector control measure [8, 40].

However, relative to LLINs, the coverage of IRS is by far the lowest across the country. Although parts of the Guinea savannah and the Transitional forest zones have had some implementation of IRS on pilot bases, studies of any such activities rolled out in the Coastal savannah are yet to be cited [32, 34, 41].

It was shown in parts of the Guinea savannah that districts where IRS was deployed compared to non-IRS districts resulted in a reduction of 39.0%, on average, in malaria incidence during the six months after spraying. These gains were, however, reversed when the IRS activities were not sustained [41, 42].

Results in this study also show that a potential increase in the reduction of predicted cases of malaria, from 48.9% to 90.4%, could be attained with an increased deployment of IRS in the various zones, for varying levels of PE of a spraying programme that takes up to five years to attain and maintain these coverage levels (80.0% and 90.0%), Additional file 2: S2 Fig. 2. At these levels of decline, pre-elimination could be in sight, as observed in the incidence rates of 1 (95% p.CI [1])/1000 population or less. This is possible if a 90.0% coverage of IRS is attained within five years and maintained up to 2030 across the country (Table 3).

IRS might hold a promise of averting more cases of malaria compared to LLINs, given the relatively low level of dependence on human behaviour for usage. However, the feasibility of rolling out IRS as an additional intervention to LLNs on a large or targeted basis may depend on the level of community acceptability, and the considerable additional cost given the limited operational budget space.

As shown in Fig. 7 and Table 4, LLIN usage in the presence of targeted IRS deployment seems to avert a substantial number of incidence cases in all zones. This reinforces the importance of using the LLIN as recommended, in order for the possible optimal benefit of malaria prevention to be realized.

Evidence from some previous field and modelling studies suggests that combining the LLINs and IRS offers higher protective effectiveness. For instance, the impact of the combination compared to only IRS was found to be OR = 0.71 (95% CI (0.59–0.86)) in Equatorial Guinea and OR = 0.63 (95% CI (0.50–0.79)) in Mozambique. Another study in Kenya reported similar results with PE of ITN and IRS, compared with ITN only, to be 62% (95% CI ( 0.50–0.72)) [43, 44]. Similarly, a cluster randomized study in the north-west of Tanzania showed that there was an enhanced benefit of combined ITN and IRS utilization. The odds of infection for a population that used ITNs in village clusters which were sprayed was reported to be considerably (two-thirds) lower than those with either ITN or IRS (OR = 0.34, 95% CI 0.23–0.53). This reduction was significantly higher compared to using ITN only (OR = 0.83), and yet greater still than reported for village clusters sprayed with IRS (OR = 0.41) only [45]. These findings are largely consistent with those reported in this study in Additional file 2: S2 Figs. 1, 2 and 3. Therefore, combining both LLINs and IRS will likely contribute very significantly, not only to averting many more predicted cases across Ghana, but to driving the annual incidence of malaria presented at the health facilities down towards pre-elimination levels if IRS coverage is scaled up across all three zones, and LLIN usage is improved substantially, a combination which has been suggested to be justified [44, 46, 47].

All investigations in this study considered hypothetical scenarios of deploying both LLINs and IRS. Moreover, IRS was considered as a supplementary intervention to LLIN. For practical and financial considerations, it may be infeasible to achieve universal coverage of both LLINs and IRS across the country. This makes efforts towards improving the effectiveness of LLIN, at the already high coverage levels, imperative; otherwise, it amounts to not achieving value for money for the investment over the years.

Therefore, as continuous efforts are being made by the NMCP and other stakeholders to scale up various vector control measures across the country, an even stronger advocacy needs to be made for education of the population through various channels such as radio, television messages and programmes, and community durbars, on the uptake of the various malaria interventions, especially LLINs [48, 49].

There are possible high levels of LLIN non-usage in Ghana, at 58.0% (2016), which is relatively higher compared to its neighbours, Benin at 28.9% (2017), Burkina Faso at 33.0% (2014) and Côte d’Ivoire at 49.6% (2016). As such, the community health officers stationed in the various Community-Based Health Planning Services (CHPS) zones may be of great use in undertaking these additional tasks of educating and mounting hang-up campaigns and other means of communication to improve the usage of LLINs [36, 38, 50, 51].

From the results thus far, it is unlikely, with the current observed rate of decline, that Ghana will achieve the principal target of reducing the burden of malaria by 75.0% (which translates to 47 cases per 1000 population per year, using cases reported in 2012 as baseline) by close of 2020, as projected in the National Malaria Strategic plan of 2014–2020, even though large declines have been achieved with malaria-attributable deaths [4]. Meeting the goals of the strategic plan by 2030 may require a full scale deployment of IRS in targeted districts and communities complementary to LLINs in all the zones to at least 80.0% coverage, using insecticides with high level of protective efficacy (Table 4).

The relatively high treatment-seeking (72.0%) and diagnosis (90.0%) levels for the Guinea savannah, transitional forest, and coastal savannah were taken into account when testing the impact of the various interventions. Attaining improved coverage levels of vector control interventions across the country will require more investment in a multi-pronged approach to roll out interventions such as LLINs and IRS (in targeted districts) to prevent cases and to treat cases concurrently. This should occur along with rallying all citizenry to improve usage of LLINs, to seek treatment promptly, and to invest in personal protection.

This study has shown that it is possible to achieve targets set out by the NMCP, and those of the Global strategy for malaria, using current interventions, if compliance to their recommended applications is improved. Therefore, any programmes and strategies which would further increase the patronage and proper and continuous use of ITN/LLIN should be encouraged and supported. As shown in the results, improvement in the coverage of LLIN only, without a corresponding improvement in usage, does not reduce the incidence of malaria in the population.

With respect to IRS, districts with incidence rates of malaria above zonal average levels could be targeted for IRS to complement LLINs, as recommended by the WHO, since the LLIN coverage is relatively high. If desired levels of malaria-related morbidity are to be attained, as projected by the National strategic policy of 2014–2020 [4], then a rapid and momentous effort needs to be made to improve upon the uptake and sustained usage of the LLINs, while consideration is given to targeted IRS, especially in high risk districts in the transitional forest and coastal savannah zones.

The findings of this study may contribute to future policy formulation for malaria control in the country.