Modeling malaria transmission in Nepal: impact of imported cases through cross-border mobility

ABSTRACT The cross-border mobility of malaria cases poses an obstacle to malaria elimination programmes in many countries, including Nepal. Here, we develop a novel mathematical model to study how the imported malaria cases through the Nepal-India open-border affect the Nepal government's goal of eliminating malaria by 2026. Mathematical analyses and numerical simulations of our model, validated by malaria case data from Nepal, indicate that eliminating malaria from Nepal is possible if strategies promoting the absence of cross-border mobility, complete protection of transmission abroad, or strict border screening and isolation are implemented. For each strategy, we establish the conditions for the elimination of malaria. We further use our model to identify the control strategies that can help maintain a low endemic level. Our results show that the ideal control strategies should be designed according to the average mosquito biting rates that may depend on the location and season.


Introduction
Malaria continues to pose a global health burden and is one of the leading causes of death in many developing countries [11]. In 2019, about 229 million cases of malaria and 409 thousand malaria-related deaths were estimated worldwide, mainly in African countries [30,67]. Despite the continuous control efforts with programmes targeting elimination, malaria cases have slightly increased globally for the past few years. According to the WHO reports, the total number of worldwide malaria cases in 2016, 2017, 2018, 2019, and 2020 are 214, 217, 219, 228, and 229 million, respectively. In pursuit of malaria elimination, broad access to human mobility has been a primary obstacle to successful malaria control in many countries, including Nepal [23]. In particular, the human mobility between low and high endemic countries results in the importation of malaria cases from high to low endemic countries, causing potentiality for the resurgence of malaria in low endemic countries. Notably, the cross-border migrants contribute to the transfer of malaria cases from high to low endemic countries [33]. For example, even though the elimination of malaria from Spain was declared in 1964, about 10,000 cases were reported, mostly in travellers and migrants, in a later year. Similarly, about 12,000 to 15,000 cases of malaria are imported to European Union (EU) every year, with the majority to France, the UK, and Germany from West Africa [32,59,68]. Given the significant obstacle to malaria elimination due to human mobility across borders, studying the impact of the imported cases through cross-border mobility on the malaria elimination programmes is critical.
Nepal is one of the countries facing the direct consequence of a cross-border transfer of malaria cases due to its open border provision with India. Although the malaria elimination programmes in Nepal started in 1958 [25], the trend of malaria cases remained fluctuated between 1963 and 2018, with a peak in 1985 (more than 42,321 cases) [22,28]. After 1985, the number of cases steadily declined, and the transmission rate eventually reached a low level of 0.08 per 1000 annual parasite incidence (API) among the risk population in 2018 [66]. With this rapidly decreasing-trend, the Nepal government has set the goal of zero indigenous malaria by 2022 and malaria elimination by 2026 [29]. However, the porous border between Nepal and India has been a severe concern for achieving these goals because even though the number of total cases declined from 2009 (3500 cases) to 2018 (1065 cases) by 69%, the net imported cases increased during this period by approximately 40 to 58% [28]. Most of these imported cases reported a history of travel to malaria-endemic areas of India [29]. An increase in imported cases has posed an uncertainty about the elimination programmes to meet Nepal's goal set. Mathematical modelling can provide a valuable tool to predict the potential impact of such imported cases on malaria elimination from Nepal.
Since the first differential equation-based model introduced by Ronald Ross in 1911 [12], various mathematical models have been developed to study the impact of control and prevention policies on the incidence of malaria in many endemic regions [6,18,19,36,42,48,54]. These models have been further extended by incorporating age structure, loss of immunity, the effect of social, economic, and environmental factors, human migration, drug resistance of vector, the impact of bed-nets, multi-groups, and multi patches [2][3][4]7,10,16,20,31,37,[45][46][47]52,[61][62][63]. Even though some mathematical models [8,17,39,51,58] include cross-border mobility, there remains uncertainty on the various aspects of the role of imported cases in vector-borne disease, particularly malaria transmission. Except for some descriptive, analytical, and retrospective studies [21,25,55,57,66], none of the previous models focused on the dynamics of indigenous and imported malaria cases in the context of Nepal, which is in critical condition of achieving the 2026 malaria elimination goal due to cross-border mobility of migrant workers.
Motivated from a previous study [64], which addressed the impact of cross-border mobility on HIV-AIDS epidemics in Nepal, we develop a novel transmission dynamics model of malaria by incorporating the imported cases through the cross-border mobility into a basic malaria model. Using the data of malaria cases in Nepal, we estimate the critical parameters of malaria dynamics in Nepal. We thoroughly analyze our model to study the impact of cross-border mobility on disease eradication and threshold dynamics.
We further use our model to predict the future trend of imported and indigenous malaria cases and evaluate the different control strategies to achieve the malaria elimination goal by 2026.

Model formulation
To develop a transmission dynamics model of malaria, we divide the total population of the home country into two groups: the population living in the home country (N hH ) and the population living abroad as migrant workers (N hM ). Each of these groups is further divided into three subgroups: susceptible (S hH ), infectious (I hH ), and recovered (R hH ) in the home country and susceptible (S hM ), infectious (I hM ), and recovered (R hM ) living abroad as migrant workers. Moreover, we consider susceptible and infectious mosquito populations in the home country (S vH , I vH ) and abroad (S vA , I vA ). We note that the migrant workers, N hM , are included in the total human population abroad (N hA ) and thus the corresponding abroad groups, susceptible (S hA ), infectious (I hA ), and recovered (R hA ) include S hM , I hM , and R hM , respectively.
In our model, malaria transmission occurs from infected mosquitoes to susceptible humans and from infected humans to susceptible mosquitoes through mosquito bites. We assume that b and b are the per capita biting rates of mosquitoes in the home country and abroad, respectively. α vh and α vh are the probability in the home country and abroad, respectively, that an infectious mosquito transmit malaria to a susceptible human in a single bite. Similarly, α hv and α hv are the probability in the home country and abroad, respectively, that the malaria is transmitted from infectious human to a susceptible mosquito in a single infectious bite. For the home country, the total number of bites (per time) from all the infectious mosquitoes is bI vH (infectious bites). Among these bites, the susceptible humans get bI vH S hH N hH infectious bites. Therefore, the incidence rate of humans (i.e. the new human infections per unit time) is bα vh I vH S hH N hH [2,14,15,34,40,71,72]. Similarly, the incidence rate for humans in abroad is b α vh I vA S hA N hA . Also, the total number of bites (per time) made by the susceptible mosquitoes in the home country is bS vH . Among these bites, the total number of bites from the infectious humans (infectious bites) is bS vH I hH N hH . Therefore, the Incidence rate of mosquitoes (i.e. the new mosquito infections per unit time) is bα hv I hH S vH N hH . Similarly, the incidence rates for mosquitoes in abroad is b α hv I hA S vA N hA . The infectious humans recover with the rate γ h , and the recovered individuals lose their immunity and move back to the susceptible class at the rate q. Because of the short lifespan of mosquitoes, we do not consider the recovered class for the mosquitoes population. The parameters and d h represent the recruitment rate and the natural death rate of humans, respectively, while the parameters φ and d v represent the recruitment rate and the death rate of mosquitoes, respectively. We assume that η represents the per capita cross-border mobility rate for susceptible populations (S hH , S hM ) and recovered populations (R hH , R hM ). Since infected individuals may behave differently in their travels from and to the home country, we take p and θ as the cross-border mobility rate for infectious individuals from and to the home country, respectively. In the model, θ I hM and bα vh I vH S hH N hH represent the imported and indigenous malaria incidence at home country, respectively.
The schematic diagram of our model is presented in Figure 1. The system of equations describing the transmission dynamics of malaria discussed above is as follows:

Approximation to incidence rate abroad
Since the detailed dynamics of malaria abroad makes the model extremely complex and uncertain, we introduce an index ψ(t) called Annual Parasite Incidence (API), for which the data are publicly available. We introduce this index into the model to approximate the incidence rate abroad. Here ψ(t) is defined as the number of positive cases of malaria per population under surveillance, i.e. ψ(t) = I hA (t) N hA (t) . The incidence rate of humans in abroad is given by Both b α vh I vA (t) and I hA (t) are differentiable functions in the interval [0, T], where T is the final time of the disease dynamics considered. Assuming that I hA (t) = 0, ∀ t ∈ [0, T], the mean value theorem of integral calculus allows us to approximate the integral of the con- I hA (t 0 ) for some t 0 ∈ (0, T). Thus, we approximate the incidence rate abroad by λ h = ζ ψ(t) and estimate the value of ζ from the data fitting. With this approximation, the system (1)-(10) reduces to the system of the following eight differential equations.

Data
In this study, we used the data containing both indigenous and imported malaria cases in Nepal. Since the total cases were not classified as imported and indigenous before 2009, we considered the data only from 2009 to 2019 for our model fitting. The primary data sources related to malaria cases in Nepal are the National Malaria Surveillance Guidelines 2019 published by the Government of Nepal, Ministry of Health and Population Department of Health Services Epidemiology and Disease Control Division (EDCD) [21,28]. In addition, we also obtained the data of Annual Parasitic Incidence (API) of India from the Malaria Site India [41,44].

Parameter estimation
The population of Nepal was estimated to be 23 [38], mostly in India. These migrant workers bring malaria upon their return home, contributing the significant number of imported malaria cases in Nepal [29]. Note that the majority of Nepalese migrants working in India are male [64]. Therefore, we took N hM (0) =  [60], the natural death rate of humans per year is taken as d h = 0.0149 per year. The number of deaths due to malaria in the base year [26] was 6, so we calculated δ h = 0.0017 per year. The duration of immunity for recovered people varies widely from region to region, and we took the immunity period to be 3 months, i.e. q = 4 per year [15]. For model fitting, we assumed that all the cases are recorded and that malaria-infected Nepalese do not move to India as workers while sick. Therefore, we took p = 0.
The population of female Anopheles mosquitoes has been estimated to be 1-10 times the human population [13,15,34]. Thus we took the mosquito population equal to base year human population 9,756,426. Based on the previous studies [1,9,13,15,34], we took the probability of disease transmission, per bite, from an infectious mosquito to a susceptible human as α vh = 0.0195, and from an infectious human to a susceptible mosquito as α hv = 0.63. Similarly, the human recovery rate and the mosquito death rate were obtained from previous studies as γ h = 1.85 per year and d v = 27.9113 per year [1,9,13,15,34]. The remaining parameters, θ, ζ , and b, were estimated from the data fitting.

Model fitting to the data
As per the national planning of malaria elimination by 2026, the Government of Nepal introduced the strategic plan in 2014, which includes the distribution of Long Lasting Insecticide Treated Nets (LLINs) and Indoor Residual Spraying (IRS) intended to reduce mosquito bites [29]. Thus in our model fitting, we allow the different biting rates for the period before (b = b 1 ) and after (b = b 2 ) 2014.
The available data are the yearly indigenous malaria incidence, the yearly imported malaria incidence, and the total malaria incidence in Nepal. From the solution of our model, the indigenous, the imported, and the total malaria incidences at time t, denoted by L(t), I(t), and T(t), respectively, can be computed using the following expressions: The model system of differential equations was solved numerically using the fourthorder Runge-Kutta method. Using the solutions, we obtained the best-fit parameters using the nonlinear least-squares regression method that minimizes the following sum of the squared residuals: where L(t k ), I(t k ), T(t k ) andL(t k ),Ī(t k ),T(t k ) are the model predicted incidences and those given in the available data. In our data fitting, we used the total 30 data points to estimate four parameters = (θ , ζ , b 1 , b 2 ). The ratio of data to the free parameters used in our model, i.e. 7:1, is well within the recommended range of 5:1-10:1 [53]. Also, three types of data (indigenous, imported, and total) included in fitting provide additional feature of malaria infection. To obtain the confidence limits for the estimated parameters, we computed standard errors from the sensitivity matrix S using the techniques described previously [49]. Furthermore, we computed the rank of the matrix S T S and found the matrix to be of the full rank (rank = 4), which ascertain the identifiability of these parameters of the model [43]. All computations were carried out in MATLAB (The MathWorks, Inc.).
In Figure 2, we present the model prediction, along with the data, of the indigenous, the imported, and the total malaria incidence. The model fits have captured the dynamics pattern of the multiple data well, and the model prediction is also consistent with the cumulative data ( Figure 2), thereby validating the model. All estimated parameters, as well as the fixed parameters, are provided in Table 2. As indicated by the data [29], the model solutions also show the decreasing trend of the malaria cases from 2009, with the indigenous case being more than the imported case until 2014. However, after 2014, the imported case overtook the indigenous case, indicating the alarming situation originating from the imported cases.

Model analysis
Note that our model is non-autonomous due to the presence of time-dependent parameter ψ(t). Since ψ(t) depends on the policy implemented abroad, the time-dependent nature of this parameter remains unknown, and the analysis of this non-autonomous model is complicated and uncertain. Therefore, for the purpose of analysis, we consider the autonomous form of the model by taking a constant k = (ζ /T) T 0 ψ(t) dt as an approximation to the incidence rate abroad.

Basic properties of model: positivity and boundedness
In this section, we show that the solutions of all the state variables are non-negative and bounded in order to demonstrate that the model is well-posed and biologically valid for describing malaria transmission dynamics. The results are presented in the following theorem. (18) is always non-negative and bounded.
Using the above conditions, we derive that for any > 0, there exists t > 0 such that the solution of the system with t ≥ t lies in the compact set

Existence of equilibria
For convenience, we let S hH = x, I hH = y, R hH = z, S hM = X, I hM = Y, R hM = Z, S vH = l, and I vH = m, and we take (x * , y * , z * , l * , m * , X * , Y * , Z * ) to represent the equilibrium point of the system (11)- (18). For simplicity, we assume that also for the infectious compartments, the mobility rate is equal in both ways, from home to abroad and vice versa, i.e. θ = p. Taking, the system (11)-(18) provides where P, Q 1 , Q 2 , Q 3 , Q 4 , S 1 , S 2 , T 1 , T 2 , U 1 , U 2 , K 1 , K 2 are non-negative constants with the combination of model parameters computed using Wolfram Mathematica. The closedforms of these expressions are provided in Supplementary Material A (page 1-3). With some algebraic manipulation, we obtain Then, from (21) and (23), we obtain where, Since the primary focus of our study is to evaluate the impact of imported cases via cross-border mobility on the local malaria transmission and control, we now analyze the existence of equilibria for four important cases stated based on the mobility and outside transmission parameters η, θ, and k. The cases we consider are: (I) η = 0, θ = 0, k = 0 (absence of cross-border mobility); (II) η = 0, θ = 0, k = 0 (complete protection of transmission abroad); (III) η = 0, θ = 0, k = 0 (strict border screening and isolation); and (IV) η = 0, θ = 0, k = 0 (presence of cross-border mobility, no protection, and no border screening or isolation). We now perform equilibria analysis for each of these four cases in the following subsections.

absence of cross-border mobility)
In this case, Q 1 , Q 3 , S 1 , T 1 , U 1 are zero and P = 0, K 1 = 0, implying that one root of (24) is zero, i.e. λ * h = 0. Then, from (22) and (23), we obtain a disease-free equilibrium point, E 0 , given by We now derive the epidemic threshold index, R 0 , corresponding to this disease-free equilibrium point (E 0 ) by using the second-generation matrix method [24,65] (the details are provided in Appendix A.2) and obtain We also confirm that our expression of R 0 is consistent with the one derived from the first principle [35,65] (see Appendix A.3). The other two roots of (24) are given by , is positive. This provides us with one endemic equilibrium point of the system. Similarly, if R 0 < 1, then a 2 < 0. In this case, the system provides either two positive values of λ * h , i.e. two endemic equilibrium points, if a 1 < 0 or no positive λ * h , i.e. no endemic equilibrium point, if a 1 > 0.

complete protection of transmission abroad)
In this case, Q 1 , Q 3 , T 1 , U 1 are zero, and P, S 1 , K 1 are positive, which shows one of λ * h to be zero from (24). Then from (22) and (23), we obtain another disease-free equilibrium point, E 01 , given by We also obtain the epidemic threshold index, R 1 , corresponding to this disease-free equilibrium point, E 01 , as follows (see Appendix A.4) Note that the migrant workers presumably travel less while they are infected. This implies η − θ ≥ 0 and hence R 1 ≥ R 0 in general. Similar to Case I above, we can easily verify that if R 1 > 1, we obtain only one endemic equilibrium point, and if R 1 < 1, we obtain two equilibrium points (or no equilibrium point) depending on whether a 1 < 0 (or a 1 > 0).

strict border screening and isolation)
In this case, Q 1 is 0, and K 1 , P, Q 3 , S 1 , T 1 , U 1 are positive. One root of the Equation (24) is zero, giving a disease-free equilibrium point, E 02 . However, this disease-free equilibrium condition asserts the absence of the disease only within the home country while allowing the disease among migrants abroad. The expression for E 02 is given by and the corresponding epidemic threshold index is (see Appendix A.5) .
Note that η = 0 implies R 2 = R 0 as expected. Again, as in Case I and II above, here also we obtain only one endemic equilibrium point for R 2 > 1 and two equilibrium points (or no equilibrium point) depending on whether a 1 < 0 (or a 1 > 0) for R 2 < 1.

presence of cross-border mobility, no protection, and no border screening or isolation)
In this case, a 3 = 0 implying λ * h = 0 (from (24)) and m * = 0 (from (21)). This implies that the disease-free equilibrium point does not exist, indicating that malaria eradication is not possible as long as there is a presence of cross-border mobility, absence of protection abroad, and absence of border screening and isolation.
To analyze possible endemic equilibrium points, we represent α, β, and γ to be the three possible roots of the cubic equation (24). The product of roots, αβγ = − a 3 a 0 . Since a 0 > 0 and a 3 < 0, αβγ > 0. This shows that all three roots can not be negative real numbers. Also, (24) can not have one negative real root and two complex roots because otherwise, two complex roots α = a + ib, β = a − ib and one negative real root γ provides αβγ = (a 2 + b 2 )γ < 0, which is not possible here. Thus, the Equation (24) provides at least one positive value of λ * h , and hence the system admits at least one endemic equilibrium point.
To identify whether the system has 1, 2, or 3 endemic equilibrium points, we first transform the Equation (24) in terms of the equilibrium infected humans, y * , to obtain equilibrium values of y * , i.e. y * 1 , y * 2 , y * 3 , are then given by the intersection of the curves F L (y) and F R (y) ( Figure 3). As shown in Figure 3, the slope −M 2 of the linear function F L (y), which can be explained in terms of the infection rate β h , can help determine the existence of 1, 2, or 3 equilibria. An increase in β h (i.e. a decrease in the slope of F L (y)) makes the equilibrium point y * 1 and y * 3 move to the right and y * 2 move to the left, eventually giving y * 1 = y * 2 corresponding to two equilibria y * 1 = y * 2 and y * 3 (Figure 3(b)). Increasing β h further, y * 1 and y * 2 disappear, and only one equilibrium point y * 3 exists. Since the equilibrium point y * 3 attains the highest value, we can correspond this situation to the worst-case scenario, i.e. a high endemic level. Similarly, decreasing β h (i.e. increasing the slope of the linear function F L (y)) causes y * 1 and y * 3 to move to the left and y * 2 to move to the right. At some point, y * 2 and y * 3 coincide with each other, giving only two equilibrium points y * 1 and y * 2 = y * 3 . If β h is decreased further, then y * 2 and y * 3 disappear, leaving only y * 1 as an endemic equilibrium point. Since y * 1 corresponds to the smallest equilibrium point, the case in which the only y * 1 exists can be considered as the endemic condition with the minimum burden. Therefore, increasing the slope −M 2 (for example, decreasing β h ), making it less than its threshold value (corresponding to y * 2 = y * 3 ), can be a vital control strategy to maintain the endemic at a low level.
In summary, the parameter a 3 , which is always non-positive, provides an important threshold for disease-free equilibrium to occur (a 3 = 0). As long as a 3 = 0 (i.e. a 3 < 0), there is no DFE, and the system always provides at least one endemic equilibrium. The absence of DFE with at least one endemic equilibrium can be attributable to ongoing infection abroad and the importation of malaria cases through cross-border mobility, making a 3 = 0. When a 3 = 0 (presence of DFE), 1 or 2 endemic equilibrium points exist according to the sign of other parameters (a 1 , a 2 ), which depend upon the thresholds R 0 , R 1 , or R 2 . Similarly, when a 3 < 0 (absence of DFE), 1 to 3 endemic equilibrium points exist according to the sign of other coefficients.

Stability analysis and uniform persistence
In this section, we provide some analytical results related to the stability and uniform persistence of the system, specifically, the local stability of the disease-free equilibrium points and the uniform persistence for Case-I and Case-II. In addition, for Case-III, we provide the local stability of the disease-free equilibrium point that corresponds to the absence of disease within the home country only. We were unable to prove the uniform persistence for Case-III and Case-IV because of the complexity of the model, presumably due to the absence of the overall disease-free equilibrium point in these cases. -I: η = 0, θ = 0, k = 0 (absence of cross-border mobility) We prove the local stability of the disease-free equilibrium E 0 as stated in the following theorem. From the eigenvalues of the Jacobian matrix J at E 0 (Appendix A.6), we have the following lemma. We now establish that R 0 > 1 can also provide a condition for the uniform persistence of the disease in the home country in the absence of cross-border mobility. Here, we use the following notations and definitions.

Case
In the absence of cross-border mobility, it is enough to consider only the decoupled system (11)- (15). We assume that τ (t)P is the solution maps generated by the decoupled system (11)-(15) with initial value P. We denote M ∂ = {P ∈ ∂ o : τ (t)P ∈ ∂ o }, and ω(P) = {y : τ (t)P → y as t → ∞}. We first state and prove the following three lemmas.
We are now ready to state the following theorem, which establishes the condition for malaria persistence in Nepal when cross-border mobility is absent.
Proof: Assume that R 0 > 1, then it follows from Lemma 4.3 that S(J) > 0. Let τ (t)P is the solution maps generated by the decoupled system (11)-(15) with initial value P ∈ o . Clearly, the system {τ (t)} t≥0 admits the global attractor in 5 + . From the Lemma 4.5, E 0 is a fixed point of τ (t) and acyclic in M∂, every solution in M∂ approaches E 0 . Moreover, Lemma 4.6 implies that E 0 is an isolated invariant set in and W s (E 0 ) ∩ o = φ. By the acyclicity theorem of uniform persistence for maps [73], it follows that τ (t) is uniformly persistent with respect to ( o , ∂ 0 ). Hence there exists σ > 0 such that lim t→∞ Inf I hH ≥ σ , lim t→∞ Inf I vH ≥ σ . This completes the proof.

Case-II: η = 0, θ = 0, k = 0 (complete protection of transmission abroad)
The local stability of the disease-free equilibrium E 01 , corresponding to the case when complete protection of transmission is in force outside Nepal, is given by the following theorem. We also have the following lemma. We also prove that R 1 > 1 provides the condition for uniform persistence of the disease with dynamics given by the system (11)-(18) with η = 0, θ = 0, k = 0. Here, we use the following notations and definitions.
To prove uniform persistence of {τ (t)} t≥0 with respect to ( o , ∂ o ), we need the following three lemmas.  i.e. E 01 is uniform weak repeller with τ (t).
With the help of the above lemmas, we now establish the following persistence theorem.
Proof: Assume that R 1 > 1, then it follows from Lemma 4.9 that S(J 1 ) > 0. Let τ (t)P is the solution maps generated by the system (11)-(18) with the initial value P. Clearly, the system {τ (t)} t≥0 admits the global attractor in 8 + . Here, the stable set of E 01 is W s (E 01 ) = {P ∈ d(τ (t)P, E 01 ) → 0 as t → ∞}. From the Lemma 4.11, E 01 is a fixed point of τ (t) and acyclic in M∂, every solution in M∂ approach to E 01 . Moreover, Lemma 4.12 implies that E 01 is an isolated invariant set in and W s (E 01 ) ∩ o = φ. By the acyclicity theorem of uniform persistence for maps [73], it follows that τ (t) is uniformly persistent with respect to ( o , ∂ 0 ). Hence there exist σ > 0 such that lim t→∞ Inf I hH ≥ σ , lim t→∞ Inf I hM ≥ σ , lim t→∞ Inf I vH ≥ σ . This completes the proof. : η = 0, θ = 0, k = 0 (strict border screening and isolation) In this case, the local stability of the corresponding disease-free equilibrium point, E 02 , is given by the theorem below. As mentioned earlier, this disease-free equilibrium asserts the disease-free only within the home country while allowing infected migrants abroad.

Approximation with autonomous sytem
Note that our model is non-autonomous due to the time-dependent parameter ψ(t), representing the API of India. However, for analytical tractability (Subsections 4.1, 4.2, and 4.3), we approximated the model with the autonomous system. Moreover, since the future API of India can not be obtained, the simulation results for model prediction and control programmes (Section 5) are computed based on the autonomous model with the current API of India. In this section, we examine the potential error that we anticipate from the autonomous model. For this, we compared the predicted cumulative cases for both autonomous and non-autonomous systems for the period 2009-2019 (see in page-1 of Supplementary Material B). We observed that the predicted cumulative cases by the autonomous model remain within 5% of the non-autonomous model. For example, from 2009 to 2019, the difference in cumulative cases from the two model systems is only 1000 out of 20,000 base cases. Therefore, the autonomous model provides a reasonable approximation to the non-autonomous model, and the study's main finding remains the same in both models.

Approximations to the exposed class of mosquitoes and pathogen transmission from recovered humans
Because of the limited data availability, we have not included two potential phenomena: the incubation period of mosquitoes and pathogen transmission from recovered humans. However, these phenomena have been considered in some previous studies [15,47]. While these phenomena may not significantly impact the primary objective of this study, namely the impact of imported cases on the malaria elimination programme, we also considered an extended model with these two phenomena incorporated. Fitting this extended model to the data with the same initial values of state variables and parameters (Tables 1 and 2), we obtained the transmission probability from recovered human to susceptible mosquitoes per bite to be r = 0.35 and the incubation period of mosquito as 1/σ = 10 days, consistent with previous studies [15,34]. Notably, per capita mosquito biting rates of b 1 = 56 and b 2 = 48, estimated with the extended model, are within the 95% confidence interval of the estimates from the simplified model.
Moreover, the cumulative case during 2020-2026 predicted by the extended model is 1425, which is close to the estimate of 1348 by the simplified model. Similarly, the predicted new cases in the year 2026 by the extended model is 195, while that by the simplified model is 191. Therefore, the qualitative and quantitative differences between the two models with  and without the exposed class of mosquitoes and pathogen transmission from recovered humans are not significant, asserting the robustness of the simplified models(see in page 2-4 of Supplementary Material B).

Malaria epidemic prediction and potential control in Nepal: model simulations
We use our model to predict the malaria epidemic in Nepal and evaluate the potential control strategies. In particular, we focus on whether the goal of malaria elimination from Nepal by 2026 set by the Government of Nepal can be achieved with the current trend and/or potential strategies. We take the year 2020 as the base year and estimate the imported and indigenous malaria cases during the period 2020-2026, and assess the number of possible control strategies that can be implemented for malaria elimination.

Basic malaria epidemic outcome in Nepal
For the basic simulations, we take the API of India, ψ(t), a constant value corresponding to the year 2018. We compute the model predicted values of indigenous and imported new cases for 2020-2026 (Figure 4(a)). We observe that if the current trend continues, the indigenous malaria cases follow a decreasing trend, but the imported cases increase slightly. We predict the indigenous malaria cases in Nepal will decrease to a yearly incidence of 67 cases in 2026, while the imported cases will remain 124 per year in 2026. As a result, the annual total incidence will remain 191 cases in the year 2026. With this incidence rate, the cumulative indigenous cases and imported cases for the period 2020 to 2026 will reach 540 and 808, respectively, making a total of 1348 cases of malaria in Nepal in this period (Figure 4(b)). While the magnitude remains relatively low, a slightly elevated level of new cases in 2026, mainly because of the imported cases, shows that the importation of malaria cases from India might remain an obstacle to the Nepal government's goal of malaria elimination by 2026.

Impact of the transmission abroad on the epidemic in home country
As revealed in the model-predicted epidemic trend, the transmission of malaria abroad that may eventually cause higher imported cases can be a determinant factor for achieving an elimination goal by 2026. The model parameter k, which represents the impact of API of India, can be used to study how the transmission dynamics abroad can impact the epidemic outcome in Nepal. According to the data API of India has had a decreasing trend for the last few years. If the decreasing trend continues, imported cases in Nepal are expected to reduce in the coming years. Our model predicts that the malaria incidence in the year 2026 decreases linearly as the % reduction of API of India increases ( Figure 5). For example, reducing the current API (base value k = 0.1) by 50% brings down the annual malaria incidence from 191 to 95 in 2026. The linear dependency of cumulative cases on the % reduction of India's API is also seen with a 50% reduction from the base case bringing the cumulative cases from 1,348 to 869 during the period 2020-2026 ( Figure 5). These results indicate that the API of India can have an important role in the cases in the home country and eventually on the success of the Nepal government's malaria elimination goal.

Control of malaria in Nepal
We consider four potential control strategies: (1) Insecticide-treated nets (ITN), (2) Indoor Residual Spraying (IRS), (3) Border screen and isolation (BSI), and (4) Migration reduction (MR). Implementation of ITN reduces the mosquito biting rate. Assuming φ ITN represents the effectiveness of ITN (assuming 100% coverage), the implementation of this strategy transforms b → (1 − φ ITN )b in our model. Similarly, IRS increases mosquito death, transforming our model as d v → φ IRS d v , where φ IRS ≥ 1 is the enhancement of mosquito death rate due to IRS. We denote the effectiveness of BSI by φ BSI , 0 ≤ φ BSI ≤ 1 so that the implementation of this strategy results in the transformation θ → (1 − φ BSI )θ , i.e. reduction of the disease import rate θ by a proportion φ BSI . The last strategy, MR, can be attributed to promoting various employment opportunities within the country, thereby reducing the cross-border mobility for seeking employment in India. The strategies can be incorporated into our model by transforming The mosquito biting rate is one of the most critical parameters of malaria transmission. While we estimated the low biting rate from the data fitting, the estimated value is the average annual rate. In reality, the biting rate can be uncertain as it is affected by various environmental (seasonal), social, and economic factors. To include broader possible scenarios, we present the results for two different biting rates, low biting rate (base Figure 6. Condition for malaria elimination in Nepal. Threshold indices R 0 , R 1 , R 2 , a 10 , a 11 , a 12 as a function of controls φ ITN , φ IRS , φ BSI , and φ MR for a low (first row) and high (second row) mosquito biting conditions. Note that the malaria is eliminated if R 0 < 1, a 10 > 0, R 1 < 1, a 11 > 0, and R 2 < 1, a 12 > 0, respectively, where a 10 , a 11 , and a 12 are corresponding values of a 1 for case I (absence of crossborder mobility), case II (full protection of transmission abroad), and case III (strict border screening and isolation), respectively. case b = 39.5 and high biting rate (approximately two times higher than the base case, b = 100).

Control strategies for elimination
The analytical results that we proved in Subsection 4.2 inform us that malaria can be eliminated from the home country if one of the following conditions can be achieved: absence of cross-border mobility (case I in Subsection 4.2.1), full protection of transmission abroad (case II in Subsection 4.2.2), and strict border screening and isolation (case III in Subsection 4.2.3). According to our theorems, in case I, II, and III, the malaria gets eliminated if (R 0 < 1, a 10 > 0), (R 1 < 1, a 11 > 0), and (R 2 < 1, a 12 > 0), respectively, where a 10 , a 11 , and a 12 are corresponding values of a 1 for case I, II, and III, respectively. We now evaluate whether the control strategies (φ ITN , φ IRS , φ BSI , and φ MR ) can bring the model to satisfy the condition of eliminating malaria in Nepal.
Our results show that for the low biting rate condition, the elimination of malaria can be achieved in Nepal regardless of whether any of the control strategies are applied or not because R 0 < 1, R 1 < 1, R 2 < 1 and a 10 > 0, a 11 > 0, a 12 > 0 remain always true ( Figure 6, first row). However, for the high biting rate condition, R 0 < 1, R 1 < 1, R 2 < 1 and a 10 > 0, a 11 > 0, a 12 > 0 can not be achieved without the control strategies, i.e. for φ ITN = φ BSI = φ MR = 0, and φ IRS = 1 (Figure 6, second row). In this case, (φ BSI ) and (φ MR ) have no impact on R and a 1 . Therefore, the control strategies related to the infected migrant workers and the mobility across the border are not enough for malaria elimination if the biting rate is high. In the high biting rate condition, (R 0 < 1, a 10 > 0), (R 1 < 1, a 11 > 0), and (R 2 < 1, a 12 > 0), i.e. the elimination of malaria, can be obtained if the level of φ ITN is greater than 0.35, 0.65, and 0.35, respectively, or the level of φ IRS is greater than 1.45, 2.60, 1.50, respectively ( Figure 6, second row).

Figure 7.
Effects of control strategies on the annual incidence rate. The model-predicted annual incidence rate in the year 2026 for various levels of ITN, IRS, BSI, and MR control in a low biting rate scenario (first row) and a high biting rate scenario (second row).

Control strategies for minimal burden
Here we perform simulations to show how the control strategies ITN, IRS, BSI, and MR impact the annual malaria incidence in 2026 and the cumulative malaria cases for 2020-2026 (Figures 7 and 8). In the low mosquito biting rate condition (Figure 7, first row), our model predicts that the 50% effectiveness of ITN (i.e. φ ITN = 0.5) reduces the annual malaria incidence in the year 2026 from 191 to 135. As a result, the cumulative cases for 2020-2026 will be reduced from 1348 to 1001 (Figure 8, first row). An increase in IRS by 1.5 times (i.e. φ IRS = 1.5) will result in an annual incidence rate of 161 in 2026 and a cumulative cases of 1167 for 2020-2026. Similarly, 50% effectiveness of BSI and MR will reduce the annual incidence rate in the year 2026 to 114 and 100, respectively, and the cumulative cases for 2020-2026 to 903 and 889, respectively.
In a high mosquito biting rate condition (Figure 7, second row, and Figure 8, second row), our model predicts that the 50% effectiveness of ITN (i.e. φ ITN = 0.5) reduces the annual malaria incidence in the year 2026 from 4.25 million to 257 and the cumulative cases for 2020-2026 from 7.8 million to 1684. Similarly, an increase in IRS by 1.5 times (i.e. φ IRS = 1.5) will result in an annual incidence rate of 106 hundred thousand in 2026 and a cumulative cases of 128 hundred thousand for 2020-2026. 50% effectiveness of BSI and MR will reduce the annual incidence rate in the year 2026 to 4.23 million and 4.13 million, respectively, and the cumulative cases for 2020-2026 to 7.5 million and 6.98 million, respectively.
While each control strategy can maintain a minimum malaria burden, their effect may vary quantitatively. Among these control strategies, MR appears to be the most effective in controlling malaria in Nepal in low mosquito biting rate conditions, while ITN is the most effective control in the high biting rate condition. This indicates that the ideal control strategy may depend on the locations and seasons in which low or high mosquito biting rates are expected. We note that due to the existing open border relationship with a long history between Nepal and India, reducing the cross-border mobility of migrant workers may not be a viable option to implement. Therefore, the optimal border screen and isolation of returning migrant workers along with local approaches, ITN and IRS, can be the most impactful option for controlling and possibly eliminating malaria in Nepal.
In the above calculation, we assumed the 100% coverage of ITN. However, 100% is unlikely to be achieved, especially in resource-limited countries like Nepal. Thus we further observe model prediction for varying coverage and efficacy of ITN. The proportion of coverage ψ ITN , 0 ≤ ψ ITN ≤ 1, and efficacy φ ITN , 0 ≤ φ ITN ≤ 1, can be incorporated in our model transforming b → (1 − φ ITN ψ ITN )b. As presented in Figure 9, our simulations show that 100% efficacy and 100% coverage of ITN significantly reduce the malaria cases in the high mosquito biting case, but the effect is not significant in the low mosquito biting case. Since Nepal's estimated mosquito biting rate is low, even the 100% coverage and 100% efficacy will reduce malaria cases in 2026 from 191 to 121 only. Thus, in addition to ITN, optimal control strategies should also focus on adequately managing imported cases to eliminate malaria from Nepal by 2026.

Conclusion
Despite a significant decline of malaria cases worldwide, many countries are currently facing difficulty achieving malaria elimination goals from those countries, mainly due to cross-border mobility of migrant workers potentially bringing malaria from abroad. Nepal provides a typical example, which is recently suffering from higher imported cases from India through open-border, posing a severe threat to the Nepal government's goal of eliminating malaria by 2026. In this study, we developed a novel mathematical model validated by the data from Nepal and used our model to analyze the effects of cross-border mobility on the malaria elimination programmes for low-endemic countries like Nepal. Our model analyzes and simulations show that malaria can be eliminated from Nepal if strategies promoting the absence of cross-border mobility, complete protection of transmission abroad, or strict border screening and isolation are implemented. In each of these potential strategies, we formulated threshold conditions for the stability of the disease-free equilibriums, providing the level of control strategies, such as ITN, IRS, BSI, and MR, to assert the elimination of malaria from Nepal. In some cases, we mathematically proved the persistence of malaria in Nepal. In one of the cases, namely strict border screening and isolation, our unique model can provide the disease-free condition only within the home country while allowing the disease among migrants abroad. In reality, such disease-free equilibrium is the most viable condition regarding the elimination of malaria from Nepal because achieving elimination from both countries can be challenging with the control strategies by the Nepal government's policy only without making combined strategies by both countries. In addition, we used our model to thoroughly assess all control strategies considered, ITN, IRS, BSI, and MR, to maintain the low level of malaria-endemic in both low and high mosquito biting rate scenarios. Interestingly, our model predicts that MR is the most effective control strategy for the low mosquito biting rate condition, while ITN is the most effective control strategy for the high mosquito biting rate condition. These interesting results suggest that the best control strategy may depend on locations and seasons that determine whether the biting rate is low or high.
There are several limitations of our study. We approximated the incidence rate abroad (India) using the API data of India. The analysis of the full model with accurate dynamics of humans and mosquitoes abroad, along with data from India, can help improve our results. Although the frequent movement of humans and mosquitoes between bordering cities and the movement of mosquitoes through cross-border transportation may be important, we have not considered these factors in our model because the data of imported cases due to these movements are not available. Therefore, our results are more relevant to the infection importation through cross-border mobility of migrant workers. Our model parameters are estimated with the limited data of malaria cases in Nepal. Uncertainty on the model parameters can be clarified if more frequent data are available. While we estimated the mosquito population based on the previous studies, the implemented values may have some uncertainties. However, our further simulations show that changing the mosquito population size to a realistic limit does not significantly impact on the main qualitative conclusions of our study. Also, some districts of Nepal are directly connected with India making them more vulnerable in comparison to other areas. Therefore, an extension of our model may be necessary to incorporate the spatial heterogeneity of the malaria risk across districts of Nepal.
We also note that we could not prove the persistence theory in Case III (strict border screening and isolation) and Case IV (presence of cross-border mobility, no protection, and no border screening or isolation) because there is no complete disease-free equilibrium in these cases. Extensive mathematical theory may be needed to show the persistence of the disease in such complicated cases. Our analyses show that for some choice of parameters (for example, those making a 1 < 0), the disease may persist even if the threshold numbers R 0 (Case I in Subsection 4.2.1), R 1 (Case II in Subsection 4.2.2), and R 2 (Case III in Subsection 4.2.3) are less than 1, indicating a possibility of backward bifurcations. Therefore, a detailed bifurcation analysis for each case (I, II, and III) can be an essential work, which we plan to pursue in future research.
In summary, our model for malaria transmission dynamics, incorporating cross-border mobility between a low endemic country (Nepal) and a high endemic country (India), can provide important insights into an obstacle that cross-border mobility may create to malaria elimination programmes. Our analytical and simulation results informing control policies that bring malaria elimination or maintain the epidemic at a low level are helpful for policymakers if implemented in conjunction with more accurate data.
for the NMS Ph.D. Fellowship Award 2020. The work of NV was supported by NSF grants DMS-1951793, DMS-1616299, DMS-1836647, and DEB-2030479 from the National Science Foundation of USA, and the UGP Award from San Diego State University.

Disclosure statement
No potential conflict of interest was reported by the author(s).

A.2 Derivation of the epidemic index R 0 from the next generation matrix method
From the system (11)- (18), the newly infectious matrix F i and its Jacobian matrix F at the diseasefree equilibrium point E 0 are Again, the transfer matrix V i and its Jacobian matrix V at the disease-free equilibrium point, Here the dominant eigenvalue of FV −1 gives the following epidemic index.

A.3 Derivation of R 0 from the first principle method
The overall basic reproduction number (R 0 ) of malaria is equal to the geometric mean of the basic reproduction numbers of malaria transmission from an infected human to susceptible mosquitoes (R H ) and the transmission of malaria from an infected mosquito to susceptible humans (R V ). Here b N hH is the average number of bites made by a mosquito to a human in unit time. Each mosquito bites at a constant rate, whereas the rate at which humans are bitten will vary with respect to the density of mosquitoes within the area. The expected number of infected mosquitoes from an infected human in his infectious period (assuming that all mosquitoes are susceptible) is given by Similarly, the expected number of susceptible humans that become infected due to contact with one infected mosquito in its infectious period (assuming that all humans are susceptible) are given by Then, we get

A.4 Derivation of the epidemic index R 1
From the system (11)-(18), the newly infectious matrix F i and its Jacobian matrix F at the diseasefree equilibrium point E 01 are Again, the transfer matrix V i and its Jacobian matrix V at the disease-free equilibrium point E 01 are, Then the dominant eigenvalue of FV −1 gives the epidemic index R 1 :

A.5 Derivation of the epidemic index R 2
From the system (11)-(18), the newly infectious matrix F i and its Jacobian matrix F at the diseasefree equilibrium point E 02 are Again, the transfer matrix V i and its Jacobian matrix V at the disease-free equilibrium point E 02 are Then the dominant eigenvalue of FV −1 provides the epidemic index R 2 .

A.6 Proof of Theorem 4.2
The local stability of E 0 is determined by the following Jacobian matrix of (11)-(18) evaluated at E 0 : The roots of the characteristic polynomial equation of J H are The roots, λ, of the characteristic polynomial equation of the matrix J A are given by Here, e 1 , e 2 , e 3 , e 1 e 2 − e 3 are positive. Using Routh Hurwitz criteria, all the eigenvalues of J A have negative real part. Therefore, all the eigenvalues of J H are negative when R 0 < 1. Thus the diseasefree equilibrium point E 0 is locally asymptotically stable if R 0 < 1, and unstable if R 0 > 1.

A.7 Proof of the Lemma 4.4
For any (S hH (0), I hH (0), R hH (0), S vH (0), I vH (0)) ∈ o , we have from the first and fourth equations of the system, Since J 0 is an irreducible matrix with non negative off diagonal elements then S(J 0 ) is simple with an associated strongly positive eigenvector [56]. Hence the vector (I hH (t), I vH (t)) is positive for all t > 0. Again from the third equation of the system,

A.8 Proof of the Lemma 4.5
Since P ∈ M ∂ , τ (t)P ∈ M ∂ for all t ≥ 0. From the definition of M ∂ , I vH (t) = 0, ∀ t ≥ 0. Using I vH (t) = 0 in (15), it follows that I hH (t) = 0 for all t ≥ 0. Then from the first, third, and fourth equation of (11)- (15) Solving the first order linear ordinary differential equations, we have lim t→∞ S hH (t) =

A.10 Proof of the Theorem 4.8
The Jacobian matrix of (11)-(18) at E 01 is J 1 = , and D = (d h + η). Let λ be eigenvalues of the matrix J 1 , then the characteristic polynomial is where Q(λ) = λ 3 + h 1 λ 2 + h 2 λ + h 3 , This implies that λ = −d h , −d v , (−d h + 2η), −(d h + q), −(d h + 2η + q) are five eigenvalues. The coefficients h 1 is positive and both h 2 > 0, h 3 > 0 if R 1 < 1. Also, . Thus all the eigenvalues have negative real parts and hence E 01 is locally asymptotically stable if R 1 < 1. If R 1 > 1, then h 0 and h 3 have opposite signs, which implies at least one λ to be positive. Hence, the disease-free equilibrium point E 01 is unstable if R 1 > 1.

A.11 Proof of the Lemma 4.10
For any (S hH (0), I hH (0), R hH (0), S vH (0), I vH (0), S hM (0), I hM (0), R hM (0)) ∈ o , we have from the first and fourth equations of the system (11) where A(t) := + ηS hM + qR hH > 0, B(t) := β h I vH N hH + η + d h , and F(t) := β v I hH N hH + d v . Again, the Jacobian J 0 corresponding to the second, seventh, and fourth equations of the system is Since J 0 is an irreducible matrix with non-negative off-diagonal elements then S(J 0 ) is simple with an associated strongly positive eigenvector [56]. Hence the vector (I hH (t), I hM (t), I vH (t)) is positive ∀ t > 0. From the third, sixth, and eighth equations of the system (11) S hH (t) ≥ (d h +η)