MGSurvE
A Python Package to Optimize the Surveillance of Genetically-Engineered Mosquitos Released in the Field





Héctor M. Sánchez C.

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I. Mosquito-Borne Diseases


1) Current Status

2) Gene Drives

3) Surveillance in the Field

1) Current Status



					

				

				

					2) Gene Drives



					

					
				

				

					3) Surveillance in the Field


					
Given a heterogeneous environment and a limited number of traps, where should we place them?

					

				

            

            
            
            
               
				

					

						
II. Landscape Analysis


						
1) Migration Network

						
2) Migration Network with Traps

						
3) Fitness Cost

					

				

				

					1) Migration Network



					Given a function that transforms distances into migration probabilities

					
						$$\alpha(s_i\to s_j)= \kappa(D(s_i\to s_j), \rho_{bio}) * \lambda(\hat{s}_i, \hat{s}_j)$$	
					

					

					we can calculate the migration matrix of the whole landscape

					
                        $$
						\tau_{\{s_n,s_n\}} = 
						\begin{Vmatrix}
						  \alpha({s_1\to s_1}) & \alpha({s_1\to s_2}) & ... & \alpha({s_1\to s_{n-1}}) & \alpha({s_1\to s_n}) \\
						  \alpha({s_2\to s_1}) & \alpha({s_2\to s_2}) & ... & \alpha({s_2\to s_{n-1}}) & \alpha({s_2\to s_n}) \\
						  \vdots  &  & \ddots  &  & \vdots  \\
						  \alpha({s_{n-1}\to s_1}) & \alpha({s_{n-1}\to s_2}) & ... & \alpha({s_{n-1}\to s_{n-1}}) & \alpha({s_{n-1}\to s_n}) \\
						  \alpha({s_n\to s_1}) & \alpha({s_n\to s_2}) & ... & \alpha({s_n\to s_{n-1}}) & \alpha({s_n\to s_n})
						\end{Vmatrix}
						$$	
					

					

					
				

                

                    2) Migration Network with Traps



                    

						To incorporate the traps' effects we can follow a similar process but split in three parts

						
						$$
							\begin{align*} 
							\delta(s_i\to t_j) &= \hat{\eta}(D(s_i\to t_j), \hat{\rho}_{trap}) * \phi(\hat{s_i}, \hat{t_j})  &\Rightarrow \nu_{\{s_n,t_n\}}\\
							\delta(t_i\to s_j || t_i\to t_j |_{i\neq j}) &= 0 &\Rightarrow 0_{\{t_n,s_n\}}\\
							\delta(t_i\to t_i) &= 1  &\Rightarrow  I_{\{t_n,t_n\}}
							\end{align*}
						$$
						

                        With this in hand, we assemble the traps' migration matrix

                        
                        $$
							\chi_{\{s_n+t_n, s_n+t_n\}} =
							\begin{Vmatrix}
								\tau_{\{s_n,s_n\}}  &  \nu_{\{s_n,t_n\}}  \\
								0_{\{t_n,s_n\}}     &  I_{\{t_n,t_n\}}
							\end{Vmatrix}
                        $$
                        

						
                    

                

                

                    3) Fitness (or cost) Function



                    

                        This allows is to get the Markov fundamental matrix

                        
                        $$F_{\{s_n,s_n\}}=(I-\tau_{\{s_n,s_n\}})^{-1}$$
                        

                        
                            which tells us how much time we spend in any site, given that we started in any other site, before we fall into a trap.

                            Finally, we can calculate the average maximum time it would take for us to trap a mosquito from anywhere in the landscape
                        

                        
                            $$
								\begin{align*} 
								\varphi_{\{s_n\}} &=Max^{j}(F_{\{s_n,s_n\}}(i, j))\\
								\phi &= Mean(\varphi_{\{s_n\}})
								\end{align*} 
                            $$
                        

						
                    

                

            

            
            
            
               
				

					

						
III. Trap Placement Optimization


						
1) Fitness Function?

						
2) Genetic Algorithm

						
3) STP Demo

					

				

				

					1) Fitness Function?



					
					
				

				

					2) Optimization Cycle





					

					

				

				

					3) São Tomé Demo



					

					

				

            

            
            
            
               
				

					


						
IV. Current Status and Future Work


						
1) Project's Status

						
2) The Future

					

				

				

					1) Project's Status





					   
					   
					






					

						Proof of concept is working and pypi package is under development!

						(base code ~90%, docs ~75%, paper ~30%)
					



					

				

				

					2) The Future



					
					

				

				

						
 Team, and Questions


						
							

									
									
									


									Héctor M. Sánchez C.

									Elijah Bartolome, Ayden Salazar, Lillian Weng, Xingli Yu, Joanna Yoo

									John Marshall, David L. Smith

									(sanchez.hmsc@berkeley.edu | chipdelmal.github.io)
							

							

								Thanks to Conversations with: Tomas León, Jared Bennett