## 4 Discussion and Conclusions  
We addressed heterogeneity as an important aspect in disease transmission, with a severe impact on the disease prevention strategy. To that end, we used a previously described agent-based malaria  
Figure 8: **(a)** The basic reproduction number, Equation (2), and (**b**) the annual entomological inoculation rate, Equation (7), both as a function of the ivermectin probability \(p_{iv}\) and heterogeneous affinity \(\alpha\). In (**c**,**d**), we show the scatter-plots of the basic reproduction number and annual entomological inoculation rate, respectively, with the corresponding values of malaria incidence. Clearly, there is a significantly stronger correlation of malaria incidence with \(EIR\) than with \(R_{0}\). model to simulate malaria transmission in scenarios of varying heterogeneous affinity, aiming to identify parameters that potentiate transition from disease persistence to elimination. Emphasis was placed on the role of preventive interventions, such as mass administration of ivermectin treatment (possibly in combination with gametocidal therapy), as well as on the adaptability of field malaria transmission indicators to accurately predict malaria incidence. Human-to-mosquito transmission, which is directly related to the time length of positive gametocytemia (\(w_{h}\)), is fundamental in disease transmission. Mass administration of ivermectin can potentially inhibit transmission due to its lethal effect on mosquito populations, immediately after feeding, therefore not allowing for complete gametocyte maturation to take place inside the mosquito, long before completing the 10 day incubation time period. Such an effect is potentiated in the settings of high mosquito density and high human-to-mosquito transmission [22; 24; 35]. We found that in the presence of strong heterogeneity, the product between the sporozoite and the parasite infection rates becomes a reliable indicator for predicting malaria transmission, performing better than the parasite rate and sporozoite rate, separately, in identical heterogeneity settings, in particular when taking into consideration the possibility of mass administration of ivermectin. Furthermore, we found that among the classical Ross-Macdonald model metrics, EIR is much more sensitive than \(R_{0}\) in predicting malaria incidence in high heterogeneity scenarios. The assumptions of our model intend to assess the impact of heterogeneity in malaria transmission. Being an agent-based model, the assessment takes place at a qualitative level. In other words, while keeping its simplicity in order to facilitate the exploration of different scenarios, our results provide evidence, at least at a qualitative level, of the large efficiency of ivermectin administration. We described the quantitative output of our simulations, parameterized according to the knowledge about both populations and drug features, but carefully revised our claims, preventing stating quantitative conclusions, only possible with clinical trials, which is of course beyond the scope of a computational agent-based simulation. Moreover, our model is based on a stochastic agent-based model that we previously published [24]. The model is implemented based in a detailed multi-parameter calibration, including information from empirical knowledge about mosquitoes, human-mosquito interaction, and the pharmacological features of ivermectin. Here, an additional assumption is derived from a well-established concept in economy and ecology, which is strongly connected to the presence of transmission hotspots and disease super-spreading events, as we now report in the manuscript: 20% of the causes are responsible for 80% of the consequences. Our model of heterogeneity was implemented as a mathematical tool replicating that concept, in accordance to the use of the 20/80 rule in infectious disease research by [PERSON], in 1996. To better explore the robustness of our results against variations of heterogeneous affinity, we introduced a parameter that measures theat affinity.